Systems and method for assessing cellular metabolic activity

ABSTRACT

Methods and corresponding apparatus and systems for assessing cellular metabolic activity are disclosed. In one aspect, a cell can be illuminated with optical radiation in order to cause multi-photon excitation of at least one endogenous metabolic cofactor in that cell and cause the excited metabolic cofactor to emit fluorescent radiation. A detector can be used to detect the fluorescent radiation emitted by the excited endogenous metabolic cofactor. A computer processor can analyze the fluorescent radiation to derive the following parameters: (1) using a computer processor to analyze the intensity of the fluorescent radiation, (2) a fluorescence lifetime of at least one of the excited metabolic cofactor, (3) a parameter indicative of mitochondrial clustering in said cell. These parameters can be used to assess at least one metabolic process of the cell.

CROSS-REFERENCE AND RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Patent ApplicationSer. No. 62/400,430 filed Sep. 27, 2016, the teachings of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present invention relates generally to method and correspondingsystem and apparatus for assessing cellular metabolic activity, and moreparticularly, but not by way of limitation, to apparatus, system, andmethod for assessing cellular metabolic activity of a cell based onassessment of endogenous Nicotinamide Adenine Dinucleotide (Phosphate)(NAD(P)H) and Flavin Adenine Dinucleotide (FAD) in that cell.

BACKGROUND

Metabolism is responsible for many life sustaining chemical processesthat support cellular function through molecular and energetictransformations. Numerous pathways have evolved to sustain cellularbioenergetics and their balance can be critical for normal developmentand aging. Conversely, metabolic perturbations or dysfunctions are oftenimplicated in numerous diseases, including obesity, diabetes, cancer,cardiovascular and neurodegenerative disorders. Accordingly, the abilityto monitor subcellular functional and structural changes associated withmetabolism can be essential for understanding tissue development anddisease progression. However, established techniques are often eitherdestructive or require the use of exogenous agents.

Generally, metabolic responses can be highly dynamic and heterogeneousboth temporally and spatially, and this inherent heterogeneity canimpact disease development or response to treatment significantly.Traditional imaging tools for assessing metabolic activity in vivotypically require addition of exogenous agents and can often havelimited resolution and sensitivity. More sensitive, quantitativemetabolic assays, such as those based on mass spectrometry and carbonlabeling, cannot be readily performed within living cells and requirecell and tissue homogenization. Therefore, such techniques can havelimited capabilities for capturing dynamic or heterogeneous aspects ofmetabolic responses.

High resolution fluorescence imaging based approaches that rely onexogenous fluorescent probes can be sensitive to mitochondrial membranepotential or target specific cellular organelles or proteins, and can,therefore, overcome the latter limitations. However, such techniquesoften require cellular manipulations and can be confounded by artifactsrelated to the distributions of the fluorophores, especially in morecomplex, three-dimensional (3D) tissues. Therefore, quantitative,high-resolution, label-free techniques for examining metabolicprocesses, non-invasively and in vivo in 3D tissues, are needed toassist with characterizing and elucidating the role of differentmetabolic pathways in disease development, and as potential therapeutictargets.

Additionally, mitochondria can undergo trafficking, fusion, and fission,creating continuously changing networks to support mitochondrialfunction and accommodate cellular homeostasis. Aberrant mitochondrialdynamics and the corresponding changes in mitochondrial organization areincreasingly associated with a variety of human pathologies, includingneurodegenerative, metabolic, cardiovascular and neoplastic diseases.Many conventional methods for investigating mitochondrial morphology areinvasive relying, for example, on scanning electron microscopy,mitochondria-specific dyes, or genetically engineered expression offluorescent proteins. Accordingly, there is a need for improved methodsand systems for assessing mitochondrial organization and dynamics.

SUMMARY

In one aspect, a method and corresponding system and apparatus forassessing cellular metabolic activity is disclosed. The disclosed methodincludes illuminating at least one cell with optical radiation in orderto cause multi-photon, e.g., two-photon, excitation of at least oneendogenous metabolic cofactor in that cell and cause the excitedmetabolic cofactor to emit fluorescent radiation. A detector can be usedto detect the fluorescent radiation emitted by the excited endogenousmetabolic cofactor, and a computer processor can be used to analyze thefluorescent radiation to derive the following parameters: (1) anintensity of the fluorescent radiation, (2) a fluorescence lifetime ofat least one of the excited metabolic cofactor(s), and (3) a parameterindicative of mitochondrial clustering in the cell. The derivedparameters can be used to assess at least one metabolic process of thecell.

In another aspect, method and corresponding system and apparatus forassessing cellular metabolic activity includes illuminating at least onecell with optical radiation in order to cause multi-photon, e.g.,two-photon, excitation of at least two endogenous metabolic cofactors inthat cell and cause the excited metabolic cofactors to emit fluorescentradiation. A detector can be used to detect the fluorescent radiationemitted by the excited metabolic cofactors, and a computer processor canbe used to analyze the fluorescent radiation to derive the followingparameters: (1) an optical redox ratio of the at least two metaboliccofactors, (2) a fluorescence lifetime of at least one of the metaboliccofactors, and (3) a parameter indicative of mitochondrial clustering inthe cell. The derived parameters can be used to assess at least onemetabolic process of the cell.

In another aspect, a method for assessing cellular metabolic activity isdisclosed. The disclosed method includes excitation of endogenousNAD(P)H and Flavin Adenine Dinucleotide (FAD) in that cell, and,thereby, causing excited NAD(P)H and FAD to emit fluorescent radiation.The term NAD(P)H, as used herein, refers to any of NADH (NicotinamideAdenine Dinucleotide) and NADPH (Nicotinamide Adenine DinucleotidePhosphate). A detector can be used to detect the fluorescent radiationemitted by the excited NAD(P)H and FAD, and a computer processor can beused to analyze the fluorescent radiation to derive the followingparameters: (1) an optical redox ratio of NAD(P)H and FAD, (2) afluorescence lifetime of NAD(P)H, and (3) a parameter indicative ofmitochondrial clustering in said cell.

In another aspect, a method for assessing cellular metabolic activity isdisclosed. The disclosed method includes illuminating at least one cellwith laser radiation so as to cause multi-photon, e.g., two-photon,excitation of at least two endogenous chromophores in the cell, therebycausing the excited chromophores to emit fluorescent radiation. Adetector can be used to detect the fluorescent radiation emitted by theexcited chromophores and a computer processor can be used to analyze thefluorescent radiation to derive the following parameters: (1) an opticalredox ratio of said chromophores, (2) a fluorescence lifetime of atleast one of said chromophores, and (3) a measure of mitochondrialclustering in said cell. The derived parameters can be used to assess atleast one metabolic pathway of the cell.

In yet another aspect, a system for assessing cellular metabolicactivity that includes an optical radiation source (e.g., a laser), atleast one detector, and an analysis module is disclosed. The opticalradiation source can illuminate at least one cell with optical radiationsuitable for providing multi-photon excitation of at least oneendogenous cellular metabolic cofactor, e.g., NAD(P)H and/or FAD, andthe at least one detector can detect the fluorescent radiation emittedfrom the multi-photon excited cofactor(s), and generate at least onesignal indicative of the detected fluorescent radiation. The analysismodule can include a processor that is configured to receive the atleast one signal and operate on the signal to determine the followingparameters: (1) the intensity of the fluorescent radiation, (2) afluorescence lifetime of the cofactor(s), and (3) a parameter indicativeof mitochondrial clustering in the cell. In some embodiments, theoptical radiation is employed to excite at least two endogenousmetabolic cofactors. By way of example, In some such embodiments, onemetabolic cofactor can be NAD(P)H and the other cofactor can be FAD. Insome such embodiments, the analysis module is configured to determinethe following parameter: (1) an optical redox ratio of NAD(P)H and FAD,(2) a fluorescence lifetime of NAD(P)H, and (3) a parameter indicativeof mitochondrial clustering in said cell. The analysis module can befurther configured to use the above parameters to assess, e.g.,determine a change, in one or more cellular metabolic processes.

In another aspect, methods and systems that employ multiphotonmicroscopy in-vivo to generate information regarding mitochondrialorganization and dynamics are disclosed. In some embodiments, suchmethods can be utilized to determine depth dependence of a mitochondrialclustering parameter in in a tissue portion, e.g., the epidermalepithelium, which can in turn be employed as an indicator of a diseasecondition. As discussed in more detail below, in some embodiments,NAD(P)H imaging can be employed as a label-free approach to monitor thestate of mitochondria and its organization in-vivo. More specifically,in many embodiments, two-photon excitation of the NAD(P)H can be used tocause the NAD(P)H to emit fluorescent radiation, which can be detectedand analyzed in a manner discussed below to obtain information about theorganization and dynamics of mitochondria. In some embodiments, thepower spectral density of the fluorescent image can be computed and aparameter indicative of mitochondrial clustering can be extracted fromthe power spectral density, e.g., via fitting the power spectral densityto an inverse power law decay expression. In some such embodiments,prior to the extraction of the mitochondrial clustering parameter, theimage can be segmented, e.g., via removal of nuclear and interstitialspaces, and the image signal voids created by such removal can beeliminated by digitally cloning the isolated cytoplasmic intensityfluctuations into the voids.

In one aspect, a method for imaging tissue in-vivo is disclosed, whichincludes illuminating a portion of a tissue in-vivo with opticalradiation (e.g., laser radiation) so as to cause a multi-photonexcitation of at least one endogenous chromophore associated with themitochondria, thereby causing said endogenous chromophore to emitfluorescent radiation, detecting the fluorescence radiation andprocessing said detected radiation to generate an original (raw) imageof the tissue portion, segmenting the image by selecting a plurality ofpixels corresponding to a selected cellular structure and masking otherpixels in the image. By way of example, the masked pixels can correspondto cells' nuclei and/or interstitial regions between the cells. Thepixel intensities of the segmented image can be normalized followed byassigning pixel intensities to the masked pixels via digital objectcloning so as to generate a processed image. The power spectral density(PSD) of the processed image can be computed, e.g., via Fouriertransform, and the PSD can be employed to extract information about anyof biochemical state and/or organization of the cellular structure,e.g., the mitochondria.

In a related aspect, a method for imaging tissue in-vivo is disclosed,which includes focusing radiation (e.g., laser radiation) into aplurality of tissue segments at different depths in-vivo so as to causea multi-photon excitation of at least one chromophore, e.g., anendogenous chromophore, associated with the cell's mitochondria, therebycausing said endogenous chromophore to emit fluorescent radiation. Foreach of the illuminated tissue segments, the following procedures can beperformed: detecting the fluorescent radiation and processing thedetected radiation to generate a raw image of the tissue portion,segmenting the image by selecting a plurality of pixels corresponding toa selected cellular structure and masking other pixels in the image,normalizing pixel intensities in the segmented image, fillingintensities of the masked pixels via digital object cloning so as togenerate a processed image, and obtaining a Fourier transform of saidprocessed image so as to determine a power spectral density associatedwith said processed image. The power spectral density of each image canbe employed to obtain a mitochondrial clustering parameter associatedwith each interrogated depth of the tissue. Alternatively, mitochondrialorganization information can be extracted using autocorrelation-basedalgorithms in the spatial domain following the intensity normalization.An example of such auto-correlation algorithms is described in anarticle entitled “Autocorrelation method for fractal analysis innonrectangular image domains,” published by MacDonald et al. in OpticsLetters, vol. 38, issue 21, pp. 4477-4479 (2013), which is hereinincorporated by reference in its entirety.

In another related aspect, a method for imaging the epithelium isdisclosed. The disclosed method comprises generating a plurality ofmulti-photon-induced fluorescence images from a plurality epidermallayers at different depths and processing said images to obtain aparameter indicative of mitochondrial clustering for each of saidepidermal depths.

In yet another related aspect, a system for optical assessment ofcellular metabolic activity is disclosed. The disclosed system comprisesan optical radiation source for generating optical radiation and one ormore optical components that direct the optical radiation onto the atleast one cell so as to cause multi-photon excitation of at least onemetabolic cofactor in the cell. The metabolic cofactor can emitfluorescent radiation in response to the excitation. A detector candetect the emitted fluorescent radiation and generate a signalindicative of the detected fluorescent radiation and an analysis modulecan operate on the detector signal to provide an assessment of at leastone cellular metabolic process.

In other examples, any of the aspects above, or any system, method,apparatus described herein can include one or more of the followingfeatures.

The metabolic process can be any process that can directly or indirectlyinduce a change (e.g., spatially and/or temporally) in the equilibriumof at least one metabolic cofactor. Some examples of such metabolicprocesses can comprise any of glycolysis, oxidative phosphorylation,glutaminolysis, any of extrinsic and intrinsic mitochondrial uncoupling,fatty acid oxidation, and fatty acid synthesis. Further, the at leastone metabolic cofactor can comprise any of NAD(P)H and a Flavin, such asFAD.

In some embodiments, observed changes in the above parameters can beemployed to assess the changes in one or more metabolic processes. Insome embodiments in which at least two metabolic cofactors are employed,a decrease in the optical redox ratio of the cofactors can be detected.By way of example, the decrease in the redox ratio can correspond to adecrease in the intensity of the fluorescent radiation associated withone cofactor, e.g., FAD, relative to the fluorescent radiation intensityof another metabolic cofactor, e.g., (NAD(P)H). In some embodiments, theobservation of such decrease in the optical redox ratio can be employedto assess changes in one or more metabolic processes, as discussed inmore detail below. In some embodiments, the observation of an increasein the optical redox ratio can be employed to assess changes in one ormore metabolic processes.

In some implementations, the optical radiation can have a wavelength ina range of about 600 nm to about 1400 nm. Further, the fluorescentradiation can have a wavelength in a range of about 400 nm to about 650nm. Further, the multi-photon excitation can be a two-photon excitation.

Further, in some embodiments, the computer processor can be used to forma fluorescent image of said at least one cell based on said detectedfluorescent radiation. By way of example, such an image can include aplurality of pixels each having an intensity indicative of the intensityof fluorescent radiation emanating from a cellular locationcorresponding to that pixel. The computer processor can be configured toanalyze the intensity associated with a plurality of pixels in theformed image to derive the mitochondrial clustering parameter. Forexample, the computer processor can perform the following steps:segmenting the image by selecting a plurality of pixels corresponding tomitochondria and masking other pixels in the image, normalizing pixelintensities in the segmented image, assigning an intensity for each ofthe masked pixels via digital object cloning so as to generate aprocessed image, obtaining a Fourier transform of the processed image soas to determine a power spectral density associated with the processedimage, and using the power spectral density to compute the mitochondrialclustering parameter. In some embodiments, the masked pixels cancorrespond to pixels not associated with the cellular mitochondria. Byway of example, the masked pixels can be associated with a nucleus ofthe cell.

In some embodiments, using the power spectral density can comprisefitting the power spectral density to an inverse power law decayexpression for computing the clustering parameter. The fitting of thepower spectral density can comprise fitting the power spectral densityby the following relation: R(k)=Ak^(−β), where k denotes spatialfrequency, A is an amplitude parameter, and β denotes the mitochondrialclustering parameter.

Further, in some embodiments, the PSD can be employed to compute amitochondrial clustering parameter. In some such embodiments, the PSDcan be fitted to an inverse law decay expression for computing theclustering parameter. For example, the PSD can be fitted to thefollowing relation: R(k)=Ak^(−β), where k denotes spatial frequency, Ais an amplitude parameter and β denotes the mitochondrial clusteringparameter.

The cell can be any cell type. Some examples of cell types comprise anyof an epithelial cell, a fibroblast, a stem cell, an adipocyte, amyofibroblast, an osteocyte, a keratocyte. For example, in someembodiments, the cell can be a diseased cell. The diseased cell can be,for example, a cancer cell. In some embodiments, at least one cell canbe illuminated in vivo. In some embodiments, the present methods andsystems are employed to assess metabolic activity of a plurality ofcells forming a tissue or an organ, e.g., epithelium such as epidermis.

In some embodiments, one or more filters can be applied to the originalimage or the processed image to minimize signal contributions to theimage from one or more chromophores other than a chromophore ofinterest, which can enable visualization of the mitochondria. By way ofexample, such a filter can be Shanbhag's entropy filter. In someembodiments, the endogenous chromophore can be NAD(P)H and the filtercan be employed to minimize contributions of any of collagen, elastin,keratin and melanin to the image. It should be understood the presentteachings can be employed to process any type of fluorescence image thatcan provide mitochondrial contrast. In some embodiments, the image canbe obtained by detecting fluorescence from endogenous chromophores, suchas two-photon-excited fluorescence from NAD(P)H. In other embodiments, amitochondrial probe, such as a fluorescence dye that stains themitochondria, can be used, and a single or multi-photon fluorescentimage of the probe can be detected and analyzed in accordance with thepresent teachings to obtain information about the mitochondria.

Further in some embodiments, segmenting the image to isolate regions ofinterest can include applying at least one bandpass filter to the image.By way of example, the bandpass filter can be generated via combinationof two or more high-pass and low-pass Gaussian filters and/orButterworth filters.

In some embodiments, the imaging methods and systems according to thepresent teachings can be employed to derive the depth dependence of themitochondrial clustering parameter, or any combination of the metaboliccofactors disclosed herein (e.g., endogenous NAD(P)H and FAD), e.g., inan epithelial tissue, such as the epithelium, e.g., the epidermis. Suchdepth dependence of the clustering parameter can in turn be employed toassess whether a disease condition, such as cancer, is present. Forexample, in healthy epidermal epithelia, the basal and parabasal layerscan display high and stable values of the clustering parameter. Inparticular, as the epithelial cell differentiation progresses from thebasal to the higher epidermal layers, the clustering parameter showsdeclining values, reaching its minimum within the spinous layer (tubularmitochondria). Further, towards the most terminal differentiation stateas the granular keratinocytes can enter an apoptotic state to create thestratum corneum, the mitochondrial clustering parameter values start torecover, signifying a return to a more fissioned phenotype.

In some embodiments, the fluorescence images can be generated byfocusing an optical radiation (e.g., laser radiation) at a plurality oflocations in different depths of the epithelium so as to causemulti-photon excitation of NAD(P)H in one or more cells and induceemission of fluorescent radiation from the excited NAD(P)H. Thefluorescent radiation emanating from each of the epidermal depths can bedetected and the detected radiation can be processed to generate aplurality of mitochondrial images each corresponding to one of thedepths. For each of the images, a mitochondrial clustering parametercorresponding to one of the depths can be extracted. Further, adepth-dependence of the mitochondrial clustering parameter can beutilized to assess whether a disease condition is present in the imagedepithelium.

Other aspects and advantages of the invention can become apparent fromthe following drawings and description, all of which illustrate theprinciples of the invention, by way of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the invention described herein, together withfurther advantages, may be better understood by referring to thefollowing description taken in conjunction with the accompanyingdrawings. The drawings are not necessarily to scale, emphasis instead isgenerally placed upon illustrating the principles of the invention.

FIG. 1A is a flow diagram of procedures for assessing a cellularmetabolic process according to some embodiments disclosed herein.

FIG. 1B is a flow diagram of procedures for assessing a cellularmetabolic activity according to some embodiments disclosed herein.

FIG. 1C schematically illustrates a system for assessing one or moremetabolic processes.

FIGS. 1D-1F schematically illustrate examples of changes in NADH and FADconcentrations due to changes in metabolic processes.

FIGS. 2A-2H schematically illustrate images corresponding to the dataused in performing the examples described herein.

FIGS. 3A-1 through 3D-3 illustrate images of the raw data, for HumanForeskin Heratinocyte (HFK) cells under metabolic pathways of glycolysisor glutaminolysis, which have been used to arrive at the resultspresented below in FIGS. 4A through 4I.

FIGS. 4A-4I illustrate examples optical readouts obtained from humanforeskin keratinocytes (HFK) under metabolic pathways of glycolysis orglutaminolysis.

FIGS. 5A-1 through 5D-2 illustrate images of raw dataset for HL-1 cellsthat have been used to produce the images shown in FIGS. 6A-1 through6F.

FIGS. 6A-1 through 6F schematically illustrate the optical readouts ofHL-1 cardiomyocytes in response to chemical uncoupling by carbonylcyanide m-chlorophenyl hydrazine (CCCP).

FIGS. 7A-7F schematically illustrate image segmentation of cytoplasm andlipids by taking into account both Flavin Adenine Dinucleotide (FAD)fluorescence intensity and Nicotinamide adenine dinucleotide (NADH)bound fraction.

FIGS. 8A-1 through 8H-2 illustrate the ex vivo and in vivo raw datasetfor brown adipose tissue (BAT) under cold activation that has been usedto generate representative images shown in FIGS. 9A-1 through 9O.

FIG. 9A-9O schematically illustrate the ex vivo and in vivo opticalreadouts of brown adipose tissue (BAT) in response to cold activation.

FIGS. 10A-1 through 10D-3 schematically illustrate images of the rawdata for a C2C12 mouse myoblast cell line under metabolic pathway of βoxidation, which are used to arrive at images shown in FIGS. 12A-1through 12F.

FIGS. 11-1 through 11-3 illustrate fluorescence images of C2C12 cells.

FIGS. 12A-1 through 12F illustrate optical readouts of C2C12 myoblastsunder β oxidation induced by supplementing Oleate or Palmitate.

FIGS. 13A-1 through 13D-2 schematically illustrate images of rawdatasets for mesenchymal stem cells (MSCs) during lipogenesis.

FIGS. 14A-1 through 14F schematically illustrate images of opticalreadouts of mesenchymal stem cells (MSCs) during metabolic pathway oflipogenesis.

FIGS. 15A-15F schematically illustrate examples of classifications ofmetabolic pathways obtained using one or two optical metrics.

FIG. 16 illustrates a table that includes the individual heterogeneityindex for optical metrics disclosed herein.

FIGS. 17A-17D-6 schematically illustrate examples of holisticvisualization of dataset using optical metrics disclosed herein.

FIG. 18 is a flow diagram of procedures that can be used for focusingradiation into one or more tissue segments in accordance with someembodiments disclosed herein.

FIG. 19 is another flow diagram of procedures that can be used forfocusing radiation into one or more tissue segments in accordance withsome embodiments disclosed herein.

FIG. 20 schematically depicts a system according to some embodimentsdisclosed herein.

DETAILED DESCRIPTION

The embodiments discussed below are illustrative of various aspects ofthe invention. Although the embodiments are discussed primarily inconnection with obtaining information regarding the mitochondria, thepresent teachings can be applied to obtain information about othercellular structures and/or organelles. The term “about” is used hereinto indicate a plus or minus variation of at most 10% around a numericalvalue. The term “substantially” is used herein to indicate a plus orminus deviation from a complete state or condition by less than 10%.

The present disclosure relates to a quantitative method andcorresponding system and apparatus for non-invasively detectingfunctional and structural metabolic biomarkers, for example by relyingon endogenous two-photon excited fluorescence from coenzymes, such asNAD(P)H and FAD.

In some embodiments, a multi-parametric analysis of the cellular redoxstate is disclosed. Specifically, cellular redox state can be analyzedby relying on NADH fluorescence lifetime and mitochondrial clusteringwithin intact living cells and 3D tissues, which are subjected tometabolic perturbations that trigger changes in distinct metabolicprocesses, including glycolysis and glutaminolysis, extrinsic andintrinsic mitochondrial uncoupling, fatty acid oxidation and synthesis.These optical biomarkers can be used to obtain complementary informationregarding the underlying biological mechanisms. The information obtainedfrom these biomarkers can further be used to achieve sensitive andlabel-free identification of metabolic processes (e.g., variousmetabolic pathways), and characterize the heterogeneity of the elicitedresponses with single cell resolution.

Two-photon excited fluorescence (TPEF) can be a powerful modality forsensitive, quantitative, label-free, and high resolution assessments ofmetabolic activity and cellular responses both in vitro and in vivo.NAD(P)H and FAD are two key metabolic co-enzymes that are involved inseveral important metabolic pathways. These metabolic co-enzymes can beused as the sources of optical contrast for many optical metabolicassessments.

Further, the TPEF intensity ratio of these two fluorophores can be usedas a metric for determining cellular redox status. Specifically, theoptical redox ratio, defined as the TPEF intensity of FAD/(NAD(P)H+FAD)can be highly correlated with mass spectrometry-based assessments ofboth FAD/(NADH+FAD) and NAD⁺/(NADH+NAD⁺). This optical redox ratio canbe an indicator that the FAD/TPEF signal is in equilibrium with thecellular NAD content. The fluorescence lifetime of NAD(P)H can also beemployed as a metabolic indicator metric, since this lifetime can dependon whether NAD(P)H is in its free or bound state. For example, longercharacteristic fluorescence lifetimes can vary over approximately 1 to 6nanoseconds, depending on the specific identity of the complex to whichNADH is bound.

These metrics (e.g., intensity ratio and fluorescence lifetime) can alsobe sensitive to processes and complementary aspects of cellularmetabolism. For example, differentiation and apoptosis and changes inthe values of these metrics can relate to alterations in the relativelevels of oxidative phosphorylation, glycolysis, glutaminolysis, andfatty acid synthesis. Further, when used in combination with oneanother, these metrics can serve as optical metabolic indices that canbe used to more accurately describe cellular metabolism (e.g., moreaccurately than using a single metric). For example, the intensity andlifetime redox metrics, when used in combination with one another, canbe employed to describe metabolic responses of cancer spheroids todifferent treatment regimens.

FIG. 1A is a flow diagram 100 of procedures for assessing a metabolicprocess according to some embodiments disclosed herein. As shown in FIG.1A, in some embodiments disclosed herein a cellular metabolic processcan be assessed by illuminating at least one cell with optical (e.g.,laser) radiation so as to cause multi-photon (e.g., two-photon)excitation of at least one endogenous metabolic cofactor in theilluminated cell, thereby causing the excited metabolic cofactor to emitfluorescent radiation 114.

In some embodiments, the illuminating laser radiation can have awavelength, for example, in a range of about 600 nanometers (nm) toabout 1400 nm, though other wavelengths can also be employed. In someembodiments, the wavelength of the emitted fluorescent radiation can bein a range of about 400 nm to about 650 nm, e.g., in a range of about400 nm to about 600 nm.

A detector can be used to detect the fluorescent radiation emitted bythe excited metabolic cofactors 124. The fluorescent radiation emittedby the excited metabolic cofactor can be detected using any suitabledetector. A variety of photodetectors can be employed for detecting theemitted fluorescent radiation. By way of example, one or morephotomultiplier tubes can be employed to detect the fluorescentradiation emitted by the metabolic cofactor. In some embodiments,appropriate filters can be employed to distinguish the fluorescentradiation emitted by two or more metabolic cofactors.

In some embodiments, the detected fluorescent radiation emitted by themetabolic cofactors can be used to determine the following parameters 1)the intensity of fluorescent radiation 134, 2) a fluorescence lifetimeof the metabolic cofactor 144, and 3) a parameter indicative ofmitochondrial clustering in the cell 154. In some embodiments, thefluorescent intensity is determined as a function of the intensities ofpixels in a fluorescent image of the cell. For example, the fluorescentintensity can be determined based on the average fluorescent intensitiesof the NAD(P)H and FAD from the pixels within the field of view. In someembodiments, a sum or a function of the sum of the intensities of pixelsin a fluorescent image of the cell can be used. These parameters can beused to access at least one metabolic process of the illuminated cell164. For example, as described in further details below, observedchanges in these parameters can be correlated to a change (e.g., anenhancement or reduction) in the activity of a metabolic process. Insome embodiments, the use of these three optical parameters (herein alsoreferred to as optical indices) together, rather than one or two of themin isolation, can result in unambiguously assessing the activity of ametabolic process.

FIG. 1B is a flow diagram 100′ of procedures for assessing cellularmetabolic activity according to some embodiments disclosed herein. Asshown in FIG. 1B, in some embodiments disclosed herein cellularmetabolic activity can be assessed by illuminating at least one cellwith laser radiation so as to cause multi-photon (e.g., two-photon)excitation of endogenous NAD(P)H and endogenous FAD in the illuminatedcell, thereby causing said excited NAD(P)H and FAD to emit fluorescentradiation 115.

This fluorescent radiation is herein referred to as “two-photon excitedfluorescence.” In some embodiments, a tissue can be illuminated in vivoso as to excite, via multi-photon excitation, endogenous NAD(P)H and FADin one or more cells of the tissue. In some other embodiments, one ormore cells can be illuminated ex vivo so as to cause multi-photonexcitation of endogenous NAD(P)H and FAD present in those cells.

In some embodiments of the above methods, the illuminating laserradiation can have a wavelength, for example, in a range of about 600 nmto about 1400 nm nanometer (nm), though other wavelengths can also beemployed. In some embodiments, the wavelength of the emitted fluorescentradiation can be in a range of about 400 nm to about 650 nm, e.g., in arange of about 400 nm to about 600 nm.

A detector can be used to detect the fluorescent radiation emitted bythe excited NAD(P)H and FAD 125. The fluorescent radiation emitted bythe excited NAD(P)H and FAD can be detected using any suitable detector.A variety of photodetectors can be employed for detecting the emittedfluorescent radiation. By way of example, one or more photomultipliertubes can be employed to detect the two-photon excited fluorescenceemitted by the NAD(P)H and FAD. In some embodiments, appropriate filterscan be employed to distinguish the two-photon excited fluorescenceemitted by NAD(P)H from that emitted by FAD.

The intensities of the detected two-photon excited fluorescence emittedby the NAD(P)H and FAD can be used to determine an optical redox ratio135. For example, the optical redox ratio can be determined according tothe following relation:

${{ORR} = \frac{I_{FAD}}{I_{NA{D{(P)}}H} + J_{FAD}}},$

where ORR denotes the optical redox ratio, I_(NADH) and I_(FAD) denote,respectively, the two-photon excited fluorescence intensities associatedwith NAD(P)H and FAD. In other embodiments, the optical redox ration canbe calculated as a ratio of the fluorescence intensity of NAD(P)Hrelative to the fluorescence intensity of FAD. In general, an opticalredox ratio of two metabolic cofactors is indicative of a ratio (e.g., adirect or a normalized ratio) of the fluorescent intensities of thosecofactors.

Further, the temporal variation of the two-photon excited fluorescenceemitted by NAD(P)H can be used to determine the fluorescence lifetime ofthe NAD(P)H fluorescent radiation 145. For example, in some embodiments,the fluorescence lifetime can be fit using an exponential decayfunction. The simplest decay model is a single exponential function,which can be described by a single decay time. In many cases, however,the decay profiles can be modeled by sums multiple, e.g., two or three,exponential functions. By way of example, a decay function F(t) can bedefined as follows where a_(i) denotes the amplitude coefficient of eachfunction and τ_(i) denotes the decay time associated with the i^(th)decay function:

${{F(t)} = {\sum\limits_{i = 1}^{n}{a_{i}e^{- \frac{r}{\tau_{i}}}}}},{n \geq 1}$

Furthermore, the two-excited fluorescence radiation emitted by NAD(P)Hcan be employed, e.g., in a manner discussed in more detail below, toobtain a parameter indicative of clustering of the cell's mitochondria155. By way of example, in some embodiments, the NAD(P)H fluorescentradiation can be used to generate a fluorescent image of the illuminatedcell(s). For example, as discussed in more detail below, a spatialFourier transform of the fluorescent image can be obtained to determinea power spectral density associated with the processed image, and thepower spectral density can be employed to compute a mitochondrialclustering parameter. In some embodiments, the fluorescent radiationfrom FAD can be used in a similar manner to compute a mitochondrialclustering parameter.

With continued reference to the flow diagram of FIG. 1B, the above threeparameters, namely, (1) the optical redox ratio, (2) the fluorescencelifetime associated with NAD(P)H, and (3) the mitochondrial clusteringparameter, can be used to assess at least one metabolic process (e.g., ametabolic pathway) of the illuminated cell 165. For example, observedchanges in these parameters can be correlated to a change (e.g., anenhancement or reduction) in the activity of a metabolic pathway. Insome embodiments, the use of these three optical parameters (herein alsoreferred to as optical indices) together, rather than one or two of themin isolation, can result in unambiguously assessing the activity of ametabolic pathway.

By way of example, the above three optical parameters can be employed toassess glycolysis, oxidative phosphorylation, glutaminolysis, any ofextrinsic and intrinsic mitochondrial uncoupling, fatty acid oxidationand fatty acid synthesis processes. For example, a concurrent reductionin the optical redox ratio, an decrease in the fluorescence lifetime ofthe NAD(P)H, and an increase in mitochondrial clustering parameter canindicate an increase in the glycolysis level. Thus, in some embodiments,the above three optical parameters can be used to identify relativechanges in the levels of glycolysis and oxidative phosphorylation.

In some embodiments, the above parameters can be employed to assessfatty acid synthesis process. For example, a concurrent decrease in theoptical redox ratio, an increase in the fluorescence lifetime of theNADH, and an increase in mitochondrial clustering can indicate anincrease in fatty acid synthesis.

In some embodiments, the combined use of the above three opticalparameters can be advantageous in that it can allow unambiguouslyidentifying changes in a metabolic process, which may not be feasible ifonly one or two of the above parameters were to be utilized. Forexample, both enhanced glycolysis and fatty acid synthesis can lead to adecrease in the optical redox ratio and an increase in mitochondrialclustering. As such, if one were to utilize only these two parameters,one could not unambiguously correlate a decrease in the optical redoxratio and an increase in mitochondrial clustering to enhanced glycolysisor fatty acid synthesis. However, NAD(P)H fluorescence lifetime candecrease for enhanced glycolysis and can increase for enhanced fattyacid synthesis. Thus, the use of the NAD(P)H fluorescence lifetimetogether with the optical redox ratio and the mitochondrial clusteringparameter can allow the unambiguous identification of enhancedglycolysis relative to enhanced fatty acid synthesis.

FIG. 1C schematically depicts a system 140 according to an embodimentfor assessing one or more metabolic pathways. The system 140 includes anexcitation source 141, such as a laser generator that provides therequired radiation (e.g., laser radiation, excitation beam l_(ex)) forilluminating a sample, e.g., tissue in vivo. In some embodiments, thesource 141 can generate radiation (for example, laser beams) having awavelength in a range of about 600 nm to about 1400 nm. The excitationbeams l_(ex) generated by the excitation source 141 can be forwarded toa scanner 142. The scanner 142 can be any suitable scanner. For example,the scanner 142 can be a scanner that scans the excitation/laser beamsemitted by the excitation source 141 along horizontal (x) and vertical(y) directions to ensure that the excitation beams l_(ex) are incidentat desired/selected locations on the sample or specimen. The system canfurther include an objective lens 139 that is configured to focus theexcitation beams l_(ex) from the source 141 onto an object plane (notshown). The object plane can lie on, or in, a specimen or targetmaterial 143. The system can also include other elements generally usedin a two-photon microscopy system, such as lenses 147, 147′, 147″,147′″, 147″″, dichroic mirrors 146, 146′, and bandpass filters 148,148′. These elements (e.g., lenses, dichroic mirrors, and filters) canbe generally responsible for directing the excitation beam l_(ex) ontothe sample or specimen 143 and directing/forwarding the radiationemitted by the sample l_(em) onto the detectors 144, 144′. Generally,the excitation source 141, the scanner 142, the objective filter 139,the lenses, dichroic mirrors, and filters can be any suitable element ofthat kind used in any suitable or available two-photon laser microscopysystem.

The excitation beams l_(ex) emitted from the excitation source, onceincident on the sample 143, can illuminate the sample with opticalradiation. The illumination of the sample 143 with the excitation beaml_(ex) can, in turn, cause multi-photon excitation of endogenous NAD(P)Hand FAD in at least one radiated cell in the sample 143. The excitationof the endogenous NAD(P)H and FAD causes these elements to emitfluorescent radiation. The emitted radiation l_(em) can be directedthrough the dichroic mirror 146 and lens 147″. The emitted radiationl_(em) can be separated, for example by the use of a dichroic mirror146′, and divided based on wavelength and directed to respectivedetectors 144, 144′ using for example, one or more lenses 147, 147′ andone or more filters (e.g., bandpass filters) 148, 148′. Specifically, insome embodiments, the emitted radiation beam l_(em) can be divided, forexample using a beam splitter or a dichroic mirror 146′, into twoportions. Each portion of the emitted beam can further be filtered suchthat one portion w₁ of the emitted beam l_(em) corresponds thefluorescent radiation emitted by endogenous FAD and another portion w2of the emitted beam l_(em) corresponds the fluorescent radiation emittedby endogenous NAD(P)H.

The separation of the portions of the emitted beam l_(em) can be doneusing any suitable technique, for example by using a bandpass filter148, 148′. The bandpass filter 148, 148′ can be configured to filter theemitted beam l_(em) such that only the portion of the emitted beaml_(em) that corresponds to a specific/desired frequency (e.g.,frequencies corresponding to fluorescent radiation emitted by endogenousFAD or fluorescent radiation emitted by endogenous NAD(P)H) is passedthrough and forwarded to the corresponding detector 144, 144′.

As shown in FIG. 1C, the emitted beam l_(em) can be divided, using adichroic mirror 146′, into two portions and each portion can be filteredusing a corresponding filter 148, 148′. Specifically, a first portion ofthe emitted beam l_(em) can be filtered using a bandpass filter 148′ toremove all frequencies of the radiation other than the frequenciescorresponding to fluorescent radiation emitted by endogenous FAD. Thisportion of the frequencies w₁ of the emitted beam l_(em) can beforwarded to a detector 144′. Similarly, a second portion of the emittedbeam l_(em) can be filtered using a bandpass filter 148 to remove allfrequencies of the radiation other than the frequencies corresponding tofluorescent radiation emitted by endogenous NAD(P)H. This portion of thethe emitted beam l_(em) is forwarded to another detector 144. Thedetectors 144, 144′ can detect the fluorescent radiation emitted by theexcited NAD(P)H and FAD. A time-correlated single photon counting(TCSPC) system 145 can be used to determine the TPEF FAD and NAD(P)Hdecay characteristics (i.e., lifetime) and the corresponding integratedintensity of the detected FAD and NAD(P)H beams.

An analyzer 149 can analyze the fluorescent radiation, as detailedbelow, to determine factors for assessing at least one metabolic processof the cells (i.e., the at least one radiated cell) in the sample 143.For example, the analyzer can determine the following parameters: (1) anoptical redox ratio of NAD(P)H and FAD, (2) a fluorescence lifetime ofNAD(P)H, and (3) a parameter indicative of mitochondrial clustering inthe cell. These parameters can be used to determine at least onemetabolic process of the at least one cell in the sample 143. Theanalyzer can be implemented in hardware, software and/or firmware in amanner known in the art and in accordance with the present teachings.For example, the analyzer can include a processor, one or more memorymodules for storing data and/or instructions (e.g., instructions forimplementing the methods described herein) and one or morecommunications buses for connecting various components of the analyzer,among other elements.

FIGS. 1D-1F schematically illustrate examples of changes in NADH and FADconcentrations due to changes in metabolic pathways. For example, thepathways depicted in FIG. 1D, are mainly affected during hypoxia andglucose starvation. In this example, PDH denotes pyruvate dehydrogenase,LipDH denotes lipoamide dehydrogenase, ETC denotes electron transportchain, and TCA denotes tricarboxylic acid. In FIG. 1E pathways involvedin utilizing the cytosolic NADH reducing power for ATP production areillustrated. Specifically, the glycerol-3-phosphate shuttle 110 andelectron flow complexes (zoomed in and surrounded by box 101) are shown.In FIG. 1E, MDH denotes malate dehydrogenase, AST denotes aspartatetransaminase, and LDH denotes lactate dehydrogenase. In FIG. 1F,pathways focusing on fatty acid β-oxidation and fatty acid synthesis areshown. In FIG. 1F, UPC1 denotes uncoupling protein 1 (shown in zoomed inform in box 103), LCFA denotes long-chain fatty acid, FFA denotes freefatty acid, and ETF denotes electron transport flavoprotein. By way ofexample, the methods and systems according to the present teachings canbe employed to assess the activities of the above metabolic pathways.

As noted above, one of the parameters employed herein for assessing theactivity of a metabolic pathway is a parameter that is indicative of thedegree of mitochondrial clustering. Mitochondrial clustering can beextracted as a quantitative metric of mitochondrial organization, e.g.,based on an automated analysis of NAD(P)H TPEF images. This biomarkercan be sensitive to the ability of mitochondria to dynamically fuse andfission, throughout the life of a cell, to optimize energy productionand distribution or to protect the cell from insult. Specifically,mitochondrial clustering can increase when glycolytic metabolismincreases during proliferation and when mitochondria assume morefragmented phenotypes. Conversely, mitochondrial clustering can decreasewhen the rate of glutaminolysis increases and fused mitochondrialnetworks become more prevalent (as shown in FIG. 1D).

Embodiments disclosed herein can employ mitochondrial clustering in vivoand/or ex vivo to assess at least one metabolic process of the cell. Forexample, mitochondrial clustering can be used to characterize dynamicchanges in mitochondrial organization in human tissues in vivo, and inresponse to perturbations such as hypoxia and reperfusion. Mitochondrialclustering can also be employed to reveal highly reproducibledepth-dependent variations within the human skin epithelia of healthysubjects. These depth-dependent variations can correspond to distinctlevels of cellular differentiation and expression of DRP1 and hFis1,which can play a key role in the orchestration of mitochondrial fission.Cancer (e.g., melanoma and basal cell carcinoma) can abrogate thesedepth-dependent variations, likely as a result of the metabolic changesthat it invokes.

Accordingly, a wealth of highly sensitive, quantitative, structural andfunctional metabolic information can be extracted from analysis ofendogenous TPEF images that are intimately related to cellular function.However, a key limitation of implementing each one of these approachesindependently is that they can provide narrow insight regarding thespecific metabolic perturbation that leads to the change of the reportedoptical metabolic metric. For example, a lower redox ratio may be theresult of either enhanced glycolysis or fatty acid synthesis.

In order to overcome these difficulties, some embodiments describedherein employ effects of specific metabolic perturbations on two or moreoptical parameters (e.g., the optical redox ratio, the NAD(P)Hfluorescence lifetime, and mitochondrial clustering) to obtaininformation regarding metabolic functions of the cells. For example, insome embodiments, the effects of glycolysis and glutaminolysis,extrinsic and intrinsic mitochondrial uncoupling, fatty acid synthesis,and/or fatty acid oxidation on two or more (and typically all) of theabove optical parameters are employed to obtain information pertainingto metabolic functions of the cells. Since glycolysis andglutaminolysis, extrinsic and intrinsic mitochondrial uncoupling, fattyacid synthesis, and/or fatty acid oxidation are pathways that can beimplicated in a wide range of pathologies, changes detected in thecombination of two or more of these three metabolic metrics can provideunique complimentary insights and high classification accuracy on thespecific type of metabolic perturbation experienced by the cellsexamined.

As described in more details below, the combined use of two or more ofthese optical metabolic metrics can serve as an important resource fordetecting both functional and structural information related tometabolism in a sensitive and quantitative manner. Such information can,in turn, lead to critically important insights regarding the metabolicpathways involved in the development of numerous diseases, withmetabolic involvement and the identification of new and effectivetherapeutic targets.

By way of example, enhanced glycolysis and glutaminolysis can elicitopposite changes in the biochemical and structural optical metabolicreadouts. Changes in the balance between the relative levels ofglycolysis and oxidative phosphorylation can constitute a prevalentcellular metabolic adaptation, not only in response to changing oxygenconditions, but also in response to changing biosynthetic andproliferative needs. Hypoxia and glucose starvation are two examples ofmetabolic perturbations that can have well-defined and opposite effectsin those metabolic pathways. Hypoxia can selectively inhibit oxidativephosphorylation and enhance glycolytic flux, whereas glucose starvationcan elicit the reverse effect.

FIGS. 2A-2H schematically illustrates images corresponding to the dataused in performing the examples described herein. Specifically, FIG. 2Aillustrate a raw NADH fluorescent image obtained by exciting cellularNADH with laser radiation at a wavelength of 755 nm, FIG. 2B illustratesa raw FAD fluorescent image obtained by exciting cellular FAD with laserradiation at a wavelength of 860, and FIG. 2C illustrates a redox ratiomap acquired from NADH (FIG. 2A) and FAD images (FIG. 2C).

FIG. 2D illustrates a phasor plot showing the clustering of pixels of anNADH image, according to the time decay at each pixel. As used herein,the term “phasor plot” refers to a graphical representation of thefluorescence intensity decay curve. The horizontal (x) and vertical (y)axes represent the real (letter “g” in FIG. 2D) and imaginary (letter“s” in FIG. 2D) parts of the Fourier transform of the decay curve.

Generally, the phasor of a mono-exponential decay can be represented bya point on the universal semi-circle, with 0 and infinite lifetimesrepresented by the points with (1,0) and (0,0) coordinates,respectively. For example, for a bi-exponential decay, the phasor plot,shown in FIG. 2D, can be represented by a point within a semi-circle199. The phasors depicting the decay rates of many pixels within a fieldtypically form an ellipse, whose major axis traverses the circle at thetwo points that represent the free (short) and bound (long) NADHlifetimes, and its centroid (i.e., the centroid of the ellipsoid)provides an estimate of the intensity fraction of NADH found in boundform.

FIG. 2E illustrates a NADH bound fraction map acquired from the phasoranalysis. FIG. 2F illustrates the binary mask of an NADH image, in whichthe cytoplasm region of cells are selected. FIG. 2G illustrates theclone stamped image of the NADH intensity signals within binary maskshown in FIG. 2F. FIG. 2H illustrates the Power Spectral Density (PSD)curve of the clone stamped image along with fitting curve. The scale barused for the plots shown in FIGS. 2A-2H is 30 μm.

FIGS. 3A-1 through 3D-3 illustrate images of the raw data, for HumanForeskin Keratinocyte (HFK) cells under metabolic pathways of glycolysisor glutaminolysis, which have been used to arrive at the resultspresented below in FIGS. 4A through 4H-3. Specifically, FIGS. 3A-1,3A-2, 3A-3 illustrate raw NADH fluorescence images obtained fromilluminating cellular NADH present in normal media, low oxygen (O₂), andno glucose HFK cells with laser radiation at a wavelength of 755 nm.

FIGS. 3B-1, 3B-2, 3B-3 illustrate raw FAD fluorescence images used togenerate redox ratio map under normal media, low oxygen (O₂), and noglucose conditions, respectively. Specifically, FIGS. 3B-1 through 3B-3are obtained from illuminating cellular FAD present in normal media, lowoxygen (O₂), and no glucose HFK cells with laser radiation at awavelength of 860 nm.

FIGS. 3C-1, 3C-2, 3C-3 illustrate phasor plots showing the clustering ofpixels in the real-imaginary (g-s) plane under normal media, low oxygen(O₂), and no glucose conditions, respectively. FIGS. 3D-1, 3D-2, 3D-3illustrate raw NADH images corresponding to the clone stampedmitochondria images shown later in FIG. 4H under normal media, lowoxygen (O₂), and no glucose conditions, respectively. The scale bar usedfor producing FIGS. 3A-1 through 3D-3 is 50 μm.

FIGS. 4A-4I illustrate examples of optical readouts obtained from humanforeskin keratinocytes (HFK) under metabolic pathways of glycolysis orglutaminolysis. As noted above, the raw data shown in FIGS. 3A-1 through3D-3 are used to generate the results shown in FIGS. 4A-4I.Specifically, FIGS. 4A-4C illustrate dynamic changes of redox ratio(FIG. 4A), bound NADH fraction (FIG. 4B), and mitochondrial organization(FIG. 4C) during the first thirty minutes after treatment. The datashown in FIGS. 4A-4C corresponds to data obtained from HFK cells undernormal media conditions 201 (FIG. 4A), 202 (FIG. 4B), 203 (FIG. 4C),data obtained under low oxygen (O₂) (i.e., hypoxia) 204 (FIG. 4A), 205(FIG. 4B), 206 (FIG. 4C), and data obtained under no glucose (i.e.,glucose starvation) 207 (FIG. 4A), 208 (FIG. 4B), 209 (FIG. 4C).

The results shown in FIGS. 4A-4I are obtained by exposing primary humanforeskin keratinocytes (HFKs) to transient hypoxia by, for example,changing the media in which the cells are normally cultured with mediathat is nitrogen bubbled for six hours. Images, specifically NADH TPEFimages, can be captured at 755 nm excitation with a non-descannedphotomultiplier tube (PMT) that is placed, for example behind a 460±20nm bandpass filter, and attached to a time-correlated single photoncounting (TCSPC) electronics module. In this manner, both the TPEF NADHdecay characteristics (i.e., lifetime) and the corresponding integratedintensity can be captured. Images, namely FAD TPEF images, can berecorded at 860 nm excitation using a 525±25 nm bandpass filter. Theoptical redox ratio can be calculated for each pixel as theFAD/(NADH+FAD) TPEF intensity. In other embodiments, the optical redoxratio can be calculated as FAD/NADH or alternatively as NADH/FAD.Generally, the optical redox ration is indicative of the relativefluorescent intensities of at least two metabolic cofactors.

FIGS. 4D-1, 4D-2, and 4D-3 illustrate representative maps of redux ratiounder normal media conditions, hypoxia, and glucose starvation,respectively. FIG. 4E illustrates the means 210, 211, 212 and standarddeviations 213, 214, 215 of the redox ratio for cells under normal mediaconditions, cells evaluated under hypoxia, and cells evaluated underglucose starvation, respectively. FIGS. 4F-1, 4F-2, and 4F-3 illustraterepresentative maps of bound NADH fraction under normal mediaconditions, hypoxia, and glucose starvation, respectively. FIG. 4Gillustrates the means 216, 217, 218 and standard deviations 219, 220,221 of the bound NADH fraction for cells under normal media conditions,cells evaluated under hypoxia, and cells evaluated under glucosestarvation, respectively. FIGS. 4H-1, 4H-2, and 4H-3 illustraterepresentative maps of clone stamped mitochondria under normal mediaconditions, hypoxia, and glucose starvation, respectively. FIG. 4Iillustrates the means 222, 223, 224 and standard deviations 225, 226,227 of mitochondrial clustering for cells under normal media conditions,cells evaluated under hypoxia, and cells evaluated under glucosestarvation, respectively. The data obtained under hypoxia treatmentconditions (FIGS. 4D-2, 4F-2, and 4H-2) are collected immediately afterhypoxia exposure. As shown, the significance symbols (**) on FIGS. 4G,and 2I reveal significant differences compared with the normal mediatreatment shown in FIG. 4E. Here, n=4 cultures/group, scale bar=50 μm,*, p<0.05, **, p<0.01.

While both NADH and NAD(P)H can contribute to the signal that isattributed to NADH, mass spectrometry results indicate that there can benegligible levels of NADPH in these cells, under these conditions. Also,mass spectroscopy results indicate that the optical redox ratio can behighly correlated with the corresponding ratio assessed based on thecorresponding concentrations of NADH and FAD. The optical redox ratiocan drop significantly and immediately upon introduction of the cells tothe hypoxic media and it can increase gradually while the oxygen contentin the media increases, as it diffuses from the micro-incubatorenvironment. Redox ratio values acquired over identical timescales fromcontrol cultures can be very stable, demonstrating that the observedchanges can be due to hypoxia (FIG. 4A).

Based on the raw NADH and FAD images (FIGS. 3A-1, 3B-1), representativeredox ratio maps from cells exposed to normal and hypoxic media (e.g.,immediately after hypoxia exposure) are shown in FIGS. 4D-1 and 4D-2. Asshown in FIG. 4D-2, cells exposed to hypoxia can have a lower redoxratio compared to the cells under normal media conditions. An example ofthis significant decrease in the redox ratio is quantified in FIG. 4E,based on four independent experiments. The decrease in redox ratio isaccompanied by a corresponding decrease in the bound NADH fraction (asshown in FIG. 4B), as quantified from the phasor-based analysis of theNADH TPEF lifetime data. This can provide a fast, graphicalrepresentation of the decay rate of the fluorescence intensity, whichcan be further processed to extract the contributions of NADH in boundform (i.e., associated with a long lifetime) relative to the total NADHTPEF signal detected.

Representative images coded by the bound NADH fraction and correspondingmean values from all experimental repeats are shown in FIGS. 4F and 4G(corresponding phasor plots shown in FIGS. 3C-1, 3C-2, and 3C-3).Finally, Fourier-based analysis of the NADH TPEF intensity images, whichcan be pre-processed to include primarily intracytoplasmic intensityvariations and can lack features associated with the nuclei and cellborders (for example, as shown in FIGS. 2F and 2H), indicates thathypoxia can lead to significantly enhanced mitochondrial clustering (asshown in FIGS. 4H, 4I, and FIGS. 3D-1, 3D-2, 3D-3). However, unlike thegradual recovery of the redox ratio (shown in FIG. 4A), coinciding withthe slow diffusion of atmospheric oxygen back into the media, theelevated mitochondrial clustering can persist for the duration ofmeasurements (shown in FIG. 4C).

This decrease in redox ratio upon the onset of hypoxia can be becausethe lack of oxygen abolishes the mitochondrial oxidative capacity andshifts the cellular metabolism to an exclusively glycolytic profile (asshown in FIG. 1D). Thus, the cytoplasmic and mitochondrial pools of NADHcan increase (as shown in FIG. 1D), leading to the observed decreasedredox ratio. A dominant contribution from the cytoplasmic, free, NADHpool can also be consistent with the observed reduction in the NADHbound fraction resulting from the analysis of the lifetime data. Thedetected increase in mitochondrial clustering can be consistent withmitochondrial fragmentation, resulting from the hypoxia drivendisengagement of the electron transport chain and the correspondingdecrease in the mitochondrial membrane potential. The persistence offragmentation while the biochemical equilibrium is under recoveryfurther agrees with the complex bioenergetics of mitochondrial fusion,which necessitate sufficient ATP availability and mitochondrial membranepolarization. This observation is also consistent with studies thatexamine mitochondrial dynamics under hypoxic conditions using bothendogenous TPEF and exogenous fluorescent mitochondrial markers.

Glucose starvation in the HFK cultures can elicit the exact oppositecombination of changes in the optical metabolic readouts than thoseobserved during the hypoxic insult. As the glycolytic flux isdiminished, mitochondrial bioenergetic adaptation can be required tosupport cellular homeostasis. To counteract the lack of the glycolyticcarbon source, pyruvate, glutamine uptake can be elevated. Glutamine canenter the mitochondria in the form of glutamate, which is converted toα-ketoglutarate and fuels the tricarboxylic acid (TCA) cycle (FIG. 1D).The abrogation of the cytoplasmic, free, NADH pools and the increasedmitochondrial oxidative flux yields an increase of the mitochondrial,bound NADH fraction (FIGS. 4B, 4F-1, 4F-2, 4F-3, 4G, 3C-1, 3C-2, 3C-3)and in the overall cellular redox ratio (FIGS. 4A, 4D-1, 4D-2, 4D-3, 4E,4G, 3A-1, 3A-2, 3A-3). The detected levels of decreased mitochondrialclustering relative to the control population (FIGS. 4C, 4H-1, 4H-2,4H-3, 4I) can show that nutrient starvation leads to mitochondrialreorganization to a more fused state (FIGS. 3D-1, 3D-2, 3D-3). Thelatter can be associated with prevention of mitochondrial autophagy andincreased oxidative efficiency to maintain ATP levels.

Extrinsic uncoupling by carbonyl cyanide m-chlorophenyl hydrazine (CCCP)can lead to an expected increase in the optical redox and mitochondrialclustering, and a less intuitive increase in the bound NADH fraction.Extrinsic and intrinsic mechanisms of mitochondrial uncoupling canaffect the optical readouts. Mitochondrial uncoupling can be animportant metabolic perturbation, as it is implicated in lifespanextension, thermogenesis, ischemic preconditioning, and other metabolicprocesses, through its effects on mitochondrial dynamics, cellularmetabolic rate, and reactive oxygen species (ROS) production. Chemicaluncoupling induced by carbonyl cyanide m-chlorophenyl hydrazine (CCCP),a proton ionophore that diminishes mitochondrial ATP production bycollapsing the proton gradient over the mitochondrial membrane, isexpected to augment the rates of glycolysis as well as the TCA (FIG.1E).

Increased glycolytic flux can be necessary to sustain ATP availabilityand produce reducing equivalents and carbon substrates that enter themitochondrial matrix and fuel the TCA cycle, which is accelerated tocompensate for the proton leak (FIG. 1E). As the redox state of thecytosolic NAD pool is a primary regulator of the glycolytic rate, thecytosolic NADH/NAD⁺ ratio is maintained at low levels for glycolysis tocontinue to occur and supply mitochondrial substrates. This can beachieved in a number of ways. For example, lactate dehydrogenase canreduce pyruvate into lactate, using NADH to perform the reduction torestore the NAD pool (FIG. 1E). Alternatively, NADH generated duringglycolysis can enter the mitochondria by the malate-aspartate shuttle(FIG. 1E), which can function effectively in that direction only whenthe NADH/NAD⁺ ratio is higher in the cytosol than in the mitochondrialmatrix, otherwise its direction is reversed. Lastly, theglycerol-3-phosphate (G3P) shuttle (FIG. 1E, box 101), alongside themalate-aspartate transporter, can provide a secondary, rapidly operatingbiochemical pathway that can be utilized for the re-oxidation ofglycolytically-formed NADH and entry of its reducing power directly inthe electron transport chain through coenzyme Q.

FIGS. 5A-1 through 5D-2 illustrate images of raw dataset for HL-1 cells(e.g., cardiac muscle cell line) under chemical uncoupling,corresponding to representative images shown in FIGS. 6A-1 through 6F.Specifically, FIG. 5A-1 illustrates the control raw NADH data and FIG.5A-2 illustrates the CCCP raw NADH data. The control raw NADH data canbe obtained from illuminating the cellular NADH present in a number ofcontrol, i.e., unexposed to treatment, cells with laser having awavelength of 755 nm. The control raw CCCP data can be obtained fromilluminating the cellular NADH present in CCCP treated cells with laserhaving a wavelength of 755 nm.

FIGS. 5B-1 and 5B-2 illustrate raw FAD images used to generatecorresponding redox ratio maps for the control and CCCP data,respectively. FIGS. 5C-1 and 5C-2 illustrate the clustering of pixels inthe g-s plane for the control and CCCP data, respectively. FIGS. 5D-1and 5D-2 illustrate the raw NADH images corresponding to the clonestamped mitochondria images shown later in FIGS. 6E-1 and 6E-2 for thecontrol and CCCP data, respectively.

FIGS. 6A-1 through 6F schematically illustrate the optical readouts ofHL31 cardiomyocytes in response to chemical uncoupling by CCCP.Specifically, FIGS. 6A-1 and 6A-2 illustrate schematic representativemaps of the redox ratio for control and CCCP data, respectively. FIG. 6Billustrates means and standard deviations of the redox ratio for controland CCCP data. FIGS. 6C-1 and 6C-2 illustrate representative maps ofbound NADH fraction for control and CCCP data, respectively. FIG. 6Dillustrates means and standard deviations of bound NADH fraction forcontrol and CCCP data. FIGS. 6E-1 and 6E-2 illustrate representativeimages of clone stamped mitochondria for control and CCCP data,respectively. FIG. 6F illustrates the means and standard deviations ofmitochondrial clustering for control and CCCP data. The significancesymbols, * and **, on top of CCCP bars demonstrate significantdifferences compared with the control group. The images are obtained bysetting n=4 cultures/group and a scale bar: 30 μm, *, p<0.05, **,p<0.01.

The CCCP-induced uncontrolled respiration can lead to a more oxidizedcellular state as expressed by the elevated optical redox ratio of HL-1mouse cardiomyocytes treated with CCCP (FIGS. 6A-1, 6A-2, 6B, 5A-1,5A-2, 5B-1, and 5B-2) versus their respective control. In this context,based on theoretical expectations, a decrease in bound NADH can beexpected as the mitochondrial NADH is consumed, but experimentallylonger NADH lifetimes can also be detected (FIGS. 6C-1, 6C-2, 6D, 5C-1,5C-2).

The dissipation of the pH gradient over the mitochondrial inner membranecan be a factor leading to mitochondrial matrix acidification. This, inturn, can affect the structural dynamics of the electron transport chainproteins that the cellular NADH can interact with in its bound form,while minimally affecting the free NADH lifetime, thus increasing theoverall contribution of the bound NADH to the observed lifetime. Otherfactors can also be involved. For example, changes in the NADH/NAD⁺ratio, which can affect the binding dynamics of the NADH related enzymesand thus their lifetime components, along with redistribution of thecellular NADH pools to enhance compensatory pathways, as discussedabove, can be in agreement with the detected higher redox ratio,decreased available cellular NADH, increased lactate production, andincreased contributions from long (>750 ps) NADH lifetimes. The latterappears to be consistent with lifetimes measured from NADH bound tomalate dehydrogenase, G3P dehydrogenase and lactate dehydrogenase.Further, changes in the rotational parameters of the mitochondrialmatrix enzymes to which NADH binds can also contribute to the detectedincreases from the longer lifetime, bound NADH.

Further, CCCP can induce mitochondrial depolarization and subsequentfragmentation, which is consistent with the detected increasedmitochondrial clustering levels (FIGS. 6E-1, 6E-2, 6F, 5D-1, and 5D-2).Also, CCCP-induced depolarization can further lead to mitochondrialmatrix condensation. A more condensed matrix can yield increasedviscosity, which is a micro-environmental parameter known to increaseNADH lifetime due to prolonged rotational diffusion time and decreasedrotational mobility.

NADH fluorescence lifetime can reveal the involvement of alternativemetabolic pathways in response to intrinsic uncoupling in brown adiposetissue vs. CCCP-induced extrinsic uncoupling. Intrinsic mitochondrialuncoupling can be performed by a number of proteins belonging to themitochondrial anion carrier family, with a subgroup named “uncouplingproteins” (UCP). The first protein identified, UCP1, is the most famousof the four and primarily mediates non-shivering thermogenesis in brownadipose tissue (BAT), acting as a dynamic long-chain fatty acid (LCFA)anion/H⁺ mitochondrial matrix symporter (as shown in FIG. 1F, Box 103).

FIGS. 7A-7F schematically illustrate image segmentation of cytoplasm andlipids by taking into account both FAD fluorescence intensity and NADHbound fraction. Specifically, FIG. 7A illustrates the NADH/lipidintensity. FIG. 7B illustrates an image of the FAD intensity. FIG. 7Cillustrates an image of an NADH/lipid bound fraction map. FIG. 7Dillustrates an image of a binary mask for the tissue area. FIG. 7Eillustrates another image of the FAD intensity. FIG. 7F illustrates aplot of the cytoplasm and lipid densities.

FIGS. 8A-1 through 8H-2 illustrate the ex vivo and in vivo raw data setfor brown adipose tissue (BAT) under the treatment of cold activation,corresponding to representative images shown in FIGS. 9A-1 through 9O.Specifically, FIGS. 8A-1, 8A-2, 8E-1, 8E-2 schematically illustrate theraw data obtained from illuminating cellular NADH at room temperature(FIGS. 8A-1, 8E-1) and by cold activation (FIGS. 8A-2, 8E-2) under exvivo (FIGS. 8A-1, 8A-2) and in vivo (FIGS. 8E-1, 8E-2) conditions. FIGS.8B-1, 8B-2, 8F-1, 8F-2 schematically illustrate raw the FAD data used togenerate redox ratio map for room temperature (FIGS. 8B-1, 8F-1) andcold activation (FIGS. 8B-2, 8F-2) under ex vivo (FIGS. 8B-1, 8B-2) andin vivo (FIGS. 8F-1, 8F-2) conditions. FIGS. 8C-1, 8C-2, 8G-1, 8G-2schematically illustrate Phasor plots of the clustering of pixels in theg-s plane under ex vivo (FIGS. 8C-1, 8C-2) and in vivo (FIGS. 8G-1,8G-2) conditions. FIGS. 8D-1, 8D-2, 8H-1, 8H-2 schematically illustrateraw NADH images corresponding to the clone stamped mitochondria imagesshown in FIGS. 9H-1, 9H-2, 9N-1, and 9N-2 under ex vivo (FIGS. 8D-1,8D-2) and in vivo (FIGS. 8H-1, 8H-2) conditions. The scale bar used forgenerating FIGS. 8A-1, 8A-2, 8B-1, 8B-2, 8D-1, and 8D-2 is 50 μm. Thescale bar used for generating FIGS. 8E-1, 8E-2, 8F-1, 8F-2, 8H-1, and8H-2 is 100 μm.

FIGS. 9A-9O schematically illustrate the optical readouts of brownadipose tissue (BAT) in response to cold activation (about 4° C.), exvivo (FIGS. 9D-1 through 9I) or in vivo (FIGS. 9J-1 through FIG. 9O).Specifically, FIG. 9A schematically illustrates the location andcomposition of BAT. The yellow circle on the mouse indicates thelocation of BAT that can be used for imaging. FIGS. 9B-1 and 9B-2schematically illustrate examples of experimental treatments that can beused. FIG. 9B-1 presents an experimental day-night 2-cycle exposure intypical room temperature conditions (e.g. ˜23 degrees C.), whereas FIG.9B-2 presents an experimental day-night 2-cycle exposure in coldtemperature conditions (e.g. ˜4 degrees C.), although other coolingtemperature conditions can be used. FIG. 9C schematically illustrates anexample of in vivo imaging of BAT, which is marked by a dashed circle901. In FIG. 9C, arrow 902 points to the main artery entering andbranching into the depots, which is a characteristic anatomical guideused for identifying the BAT tissue. In general, for mice as well ashumans, who have similar core temperatures, an ambient temperature belowabout 17-18° C. would be considered a cooling temperature. Further, theheat transfer can be achieved in a variety of different ways, e.g., air,liquid (e.g., water), a cooling vest, etc.

FIGS. 9D-1 through 9J-2 schematically illustrate representative maps ofthe redox ratio for room (FIGS. 9D-1, 9F-1, 9H-1, and 9J-1) and coldtemperature (FIGS. 9D-2, 9F-2, 9H-2, and 9J-2). FIGS. 9E and 9Kschematically illustrate the means and standard deviations of redoxratio at room and cold temperatures. FIGS. 9F-1, 9F-2, 9L-1, and 9L-2schematically illustrate representative maps of a bound NADH fraction.FIGS. 9G and 9M schematically illustrates the means and standarddeviations of a bound NADH fraction at room and cold temperatures. FIGS.9H-1, 9H-2, 9N-1, and 9N-2 schematically illustrate representativeimages of an example clone stamped mitochondria. FIGS. 9I and 9Oschematically illustrate graphs of means and standard deviations ofmitochondrial clustering. The significance symbols “*” on top of coldbars reveal significant differences compared with the control group(room temperature). For both ex vivo and in vivo experiments, n=3mice/group. The scale bar in FIGS. 9D-1, 9D-2, 9F-1, 9F-2, 9H-1, and9H-2 is 50 μm. The scale bar in 9J-1, 9J-2, 9L-1, 9L-2, 9N-1, and 9N-2is 100 μm. The significance symbol * indicates p<0.05.

In one experiment, non-shivering thermogenesis is induced by coldexposure and the impact of subsequent norepinephrine-induced activationof brown fat depots of C57BL/6 mice, both ex vivo and in vivo, isobserved (FIGS. 7A-7F, 9A, and 9C). In this experiment, consistentchanges in the optical metabolic readouts in both ex vivo and in vivocases are observed. The redox ratio (FIGS. 9D-1, 9D-2, 9E, 9J-1, 9J-2,9K, 8A-1, 8A-2, 8B-1, 8B-2, 8E-1, 8E-2, 8F-1, and 8F-2) andmitochondrial clustering (FIGS. 9H-1, 9H-2, 9I, 9N-1, 9N-2, 9O, 8D-1,8D-2, 8H-1, and 8H-2) are significantly increased, consistent with theobservations of CCCP-induced uncoupling. This can be a reflection of amore oxidized state of the activated brown fat depots due to higherturnover rates in the electron transport chain. Furthermore, adrenergicstimulation due to cold exposure is known to induce DRP1-dependentmitochondrial fragmentation prior to the depolarization associated withfree fatty acid release, UCP1 function and heat production.

Proper mitochondrial fission can be necessary for potentiatingmitochondrial depolarization and OPA-1 related cristae restructuring,leading ultimately to matrix swelling. The latter can be an indicationthat different fissioning responses between the extrinsic and intrinsicmechanisms can exist. As originally expected and contrary to the CCCPoutcomes, the NADH bound fraction in the cold-activated BAT is reduced(FIGS. 9F-1, 9F-2, 9G, 9L-1, 9L-2, 9M, 8C-1, 8C-2, 8G-1, 8G-2). Thediscrepancy between the extrinsic and intrinsic uncoupling lifetimereadouts can be attributed to the involvement of alternative metabolicpathways in the BAT tissue function, as well as the differentialmitochondrial dynamics responses affecting the matrix density.

Activated BAT tissue can primarily utilize fatty acids (for example, asshown in FIG. 1F) as a direct oxidative substrate to generate acetyl-coAand reducing equivalents (FADH₂ and NADH) to maintain the protongradient. Glycolytic fluxes are mainly driven towards cytosolic ATPproduction through lactate conversion and can also partially serve ananaplerotic function to replenish citric acid cycle intermediates (i.e.,oxaloacetate), which can in turn facilitate the capacity of the TCA tomaintain elevated levels of fatty acid oxidation. As such, thecytoplasmic-mitochondrial shuttling mechanisms described earlier (i.e.,the malate/aspartate and the G3P shuttle) are often not expected to playsignificant roles in this case.

Increased levels of free fatty acids can inhibit the mitochondrialflavin moiety of the G3P shuttle, shifting its direction to G3Pproduction, a molecule necessary for free fatty acid incorporation intotriacylglycerols (TAGs) and subsequent lipid droplet storage (thisprocess can still be active during brown adipose tissue activation). Inaddition, UCP1's uncoupling function can be dynamic, based on theavailability of free fatty acids released from the induced lipolysis(FIG. 1F). Accordingly, the degrees of uncoupled thermogenesis andrespiration can be swiftly and sensibly regulated. Further, underadrenergic stimulation and free fatty acid release, uncoupledthermogenesis and respiration can be upregulated, consuming themitochondrial NADH and FADH₂ and, thereby, increasing the redox ratioand lowering the bound fraction contributions. The decreased matrixcondensation (due to the mitochondrial swelling) can also increase therotational mobility of the enzymatic complexes and, thus, reduce theirlifetimes.

Saturated fatty acid overload can induce a significant decrease in theoptical redox ratio and bound NADH fraction and an increase inmitochondrial clustering as mitochondria becomes dysfunctional. In someembodiments, fatty acid metabolism is analyzed using fatty acid loadingand fatty acid synthesis. Fatty acid metabolism can be highly relevantin increasingly more prevalent metabolic disorders, including obesity,liver dysfunction, cardiomyopathy and diabetes. Using establishedprotocols, C2C12 mouse myoblasts with either oleate, as a representativeunsaturated fatty acid, or palmitate, a saturated fatty acid areemployed.

Both saturated and unsaturated fatty acids can be chosen since distinctoutcomes with regards to cellular parameters, including ROS and ATPproduction and mitochondrial dynamics can be obtained. During fatty acidcatabolism FADH₂, NADH and acetyl-CoA are produced sequentially untilall of the carbons of the fatty acid chain are utilized (FIG. 1F).Acetyl-CoA can normally enter the TCA cycle to complete its oxidationand production of reducing equivalents, while NADH can enter theelectron transport chain through complex I and FADH₂ through theelectron transfer flavoprotein and Q complex directly, thus bypassingcomplex I. Increased β-oxidation due to fatty acid overload canintroduce primarily an excess of mitochondrial NADH, and, therefore,reduce the optical redox ratio and increase the bound lifetimecontributions.

FIGS. 10A-1 through 10D-3 schematically illustrate images of the rawdataset for C2C12 cells (e.g., a mouse myoblast cell line) undermetabolic pathway of β oxidation, corresponding to representative imagesshown later in FIGS. 12A-1 through 12F. Specifically, FIGS. 10A-1through 10A-3 illustrate images of the raw NADH Vehicle data (FIG.10A-1, the term “Vehicle” is intended to refer to data having no addedfatty acid) and raw Oleate (unsaturated fatty acid, FIG. 10A-2) andPalmitate (saturated fatty acid, FIG. 10A-3) fatty acid data. The term“NADH data” is used herein to refer to generally data in which cellularNADH is illuminated using optical radiation. Similarly, the term “FADdata” is used herein to generally refer to data in which cellular FADhas been illuminated using optical radiation.

FIGS. 10B-1 through 10B-3 illustrate images of the raw FAD Vehicle data(FIG. 10B-1) and Oleate (FIG. 10B-2) and Palmitate (FIG. 10C-3) fattyacids data, which can be used to generate corresponding redox ratiomaps. FIGS. 10C-1 through 10C-3 illustrate images of the phasor plotsshowing the clustering of pixels in the g-s plane corresponding toVehicle data (FIG. 10C-1) and Oleate (FIG. 10C-2) and Palmitate (FIG.10C-3) fatty acid data. FIGS. 10D-1 through 10D-3 illustrate images ofthe raw NADH Vehicle data (FIG. 10D-1) and Oleate (FIG. 10D-2) andPalmitate (FIG. 10D-3) fatty acid data corresponding to clone stampedmitochondria images shown later in FIGS. 12E-1 through 12E-3.

FIGS. 11-1 through 11-3 illustrate fluorescence images of C2C12 cells(the cells were stained with a fluorescent mitochondrial dye thatemanates radiation in the green range of the spectrum). Although shownin black and white, these images are obtained with MitoTracker Green FMstaining under different fatty acids supplements. FIGS. 11-1, 11-2, and11-3 are obtained from Vehicle data and Oleate 200 μM, and Palmitate 200μM fatty acids, respectively.

FIGS. 12A-1 through 12F illustrate optical readouts of C2C12 myoblastsunder β oxidation induced by supplementing Oleate or Palmitate.Specifically, FIGS. 12A-1 through 12A-3 illustrate representative mapsof redox ratio for Vehicle (FIG. 12A-1), Oleate (FIG. 12A-2), andPalmitate (FIG. 12A-3) data. FIG. 12B illustrates the means and standarddeviations of the redox ratio. FIGS. 12C-1 through 12C-3 illustraterepresentative maps of bound NADH fraction for Vehicle (FIG. 12C-1),Oleate (FIG. 12C-2), and Palmitate (FIG. 12C-3) data. FIG. 12Dillustrates the means and standard deviations of the bound NADHfraction. FIGS. 12E-1 through 12E-3 illustrate representative maps ofclone stamped mitochondria for Vehicle (FIG. 12E-1), Oleate (FIG.12E-2), and Palmitate (FIG. 12E-3) data. FIG. 12F illustrates the meansand standard deviations of mitochondrial clustering. The significancesymbols “*” on top of adipogenic bars reveal significant differencescompared with the MSC propagation group. In this example, n=4cultures/group and scale bar is 50 μm. Significance symbol * indicatesp<0.05 and ** indicates p<0.01.

Both fatty acids (Oleate and Palmitate) can lead to a decrease in theredox ratio (FIGS. 12A-1, 12A-2, 12A-3, 12B-1, 12B-2, 12B-3, 10A-1,10A-2, 10A-3, 10B-1, 10B-2, 10B-3). However, these fatty acids can havesignificantly decreased levels of NADH bound fraction upon treatmentwith Oleate and even lower levels upon exposure to Palmitate (FIGS.12C-1, 12C-2, 12C-3, 12D, 10C-1, 10C-2, 10C-3). However, no changes inthe mitochondrial clustering of Oleate treated cells are observed,whereas Palmitate treatment could induce increased mitochondrialclustering (FIGS. 12E-1, 12E-2, 12E-3, 12F, 10D-1, 10D-2, 10D-3).

As shown in FIG. 11-3, Palmitate can induce mitochondrial dysfunctionand fragmentation, due to increased ROS production. However, as shown inFIG. 11-2, Oleate can preserve mitochondrial function and architecture.These distinct mitochondrial dynamics outcomes can be attributed to thediverse chemical characteristics of the fatty acids. Although both fattyacids can be expected to create an energetic burst, since Oleate hasdouble bonds, it can require an NADPH-mediated oxidation step. Thisoxidation step can slow the catabolic rate and steadily consume NADPH,which can be recreated by consuming the proton gradient. Moreover,unsaturated fatty acids can be more easily incorporated into TAGs andcan be chemically better mitochondrial uncouplers than their saturatedcounterparts. Therefore, Oleate can be more easily storedintracellularly and, while it can create an energetic surplus, it canalso steadily and mildly consume the proton gradient and can uncouplethe proton motive force from ATP production. This, in turn, can promotethe forward flow of electrons through the respiratory chain and decreasethe chances of Q complex competition overload by the FADH₂ and the NADHthat compete to oxidize complex Q (FIG. 1F). Therefore, the cell canevade the formation of ROS by complex I induced by reversed electrontransport function, and can preserve mitochondrial function andarchitecture.

In contrast, Palmitate can lack the beneficial characteristics describedfor Oleate and can induce rapid ROS formation, mitochondrialfragmentation, mitochondrial dysfunction, and ultimately decreased ATPproduction. This unexpected NADH lifetime reduction can be due tocytoplasmic NADH contributions from peroxisomal β-oxidation and reversedmalate-aspartate shuttle function. Peroxisomal β-oxidation can produceNADH in a similar manner, as the mitochondrial one. Re-oxidation of theintraperoxisomal NADH is necessary for the β-oxidation to continue andthat can happen in the cytosol and the mitochondria. Therefore, ashuttling mechanism can be necessary to regulate intraperoxisomalNAD⁺/NADH, transferring NADH to the cytosol. Although a peroxisomal tocytosolic shuttling mechanism has not been precisely identified yet ineukaryotes, evidence exists for a lactate/pyruvate-based redox shuttle(FIG. 1F). The cytosolic NADH can be recycled through one of the twocytosolic to mitochondria NAD(H)-redox shuttles described previously. Asnoted, the malate-aspartate shuttle can be bidirectional and can dependhighly on the cytosolic and mitochondrial NADH/NAD⁺ ratios.Mitochondrial NADH can be accumulated when mitochondrial β-oxidationlevels are high. As high mitochondrial NADH/NAD⁺ ratio can inhibitβ-oxidation, the shuttle can act in reverse, shuttling NADH to thecytoplasm.

The G3P shuttle can include a reversible NADH to G3P oxidation step andan irreversible G3P to FADH₂ reduction. During high levels of free fattyacids, the FADH₂ reduction can be attenuated, promoting the cytosolicNADH oxidation to G3P production and TAG biosynthesis. Through thismechanism, Palmitate, due to its decreased TAG incorporation, isanticipated to have an even lower bound NADH fraction than Oleate. Inthe case of Palmitate, a continuously increasing cytosolic NADH/NAD⁺ dueto palmitate's energetic burst and decreased TAG incorporation canultimately inhibit both peroxisomal and mitochondrial metabolism. Thiscontinuous and uncontrolled cellular redox decrease can be a majorparticipant in the lipotoxicity, accumulation of metabolicintermediates, and cell death observed after prolonged incubation timeswith palmitate alone.

FIGS. 13A-1 through 13D-2 schematically illustrate images of rawdatasets for mesenchymal stem cells (MSCs) during lipogenesis. The rawdata shown in FIGS. 13A-1 through 13D-2 are used to produce therepresentative images shown in FIGS. 14A-1 through 14F. Specifically,FIG. 13A-1 illustrates the raw NADH data obtained from propagation ofMSCs. FIG. 13A-2 illustrates the raw data obtained from adipogenic celldifferentiation. FIGS. 13B-1 and 13B-2 illustrate the raw FAD imagesused to generate redox ratio map for MSC propagation and adipogenicdata, respectively. FIGS. 13C-1 and 13C-2 include phasor plotsillustrating the clustering of pixels in the g-s plane. FIGS. 13D-1 and13D-2 illustrate the raw NADH images corresponding to the clone stampedmitochondria images shown later in FIGS. 14E-1 and 14E-2 for MSCpropagation and adipogenic data, respectively. The scale bar used forgenerating these images is 50 μm.

FIGS. 14A-1 through 14F schematically illustrate images of opticalreadouts of mesenchymal stem cells (MSCs) during metabolic pathway oflipogenesis. Specifically, FIGS. 14A-1 and 14A-2 include representativemaps of redox ratio for MSC propagation and adipogenic data,respectively. FIG. 14B illustrates a plot of the means and standarddeviations of redox ratio. FIGS. 14C-1 and 14C-2 include representativemaps of the bound NADH fraction for MSC propagation and adipogenic data,respectively. FIG. 14D illustrates a plot of the means and standarddeviation of bound NADH fraction. FIGS. 14E-1 and 14E-2 illustrate therepresentative images of clone stamped mitochondria for MSC propagationand adipogenic data respectively. FIG. 14F illustrates plots of themeans and standard deviations of mitochondrial clustering. Thesignificance symbols on top of adipogenic bars reveal significantdifferences compared with the MSC propagation group. In these images,n=4 cultures/group, scale bar is 50 μm, *, p<0.05; **, p<0.01.

Fatty acid synthesis can lead to the accumulation of bound NADH inmitochondria and an increase in mitochondrial clustering to facilitatebiosynthesis. The differentiation of mesenchymal stem cells intoadipocytes can be employed to determine the impact of fatty acidsynthesis. Specifically, as shown in FIGS. 14A-1, 14A-2, fatty acidsynthesis can be accompanied by a decrease in the redox ratio (also asshown in FIGS. 14B, 13A-1, 13A-2, 13B-1, 13B-2). This change can beattributed to mitochondrial biogenesis and the accumulation ofmitochondrial NADH as glucose catabolism outpaces ATP production tosupport the biosynthetic drive that consumes TCA intermediates (i.e.,citrate) (FIG. 1F). These processes can also lead to a correspondingincrease in the bound NADH fraction (FIGS. 14C-1, 14C-2, 14D, 13C-1,13C-2). The associated increase in mitochondrial clustering (FIGS.14E-1, 14E-2, 14F, 13D-1, 13D-2) is consistent with mitochondrialtruncation and branching to efficiently surround the lipid droplets andfacilitate lipid biosynthesis compared to the more extendedmitochondrial networks of the undifferentiated human mesenchymal stemcells.

Multi-parametric functional assessment can enable identification of thenature of metabolic perturbation and quantitative characterization ofthe heterogeneity in cellular responses. FIGS. 15A-15F schematicallyillustrate examples of classifications of metabolic pathways obtainedusing one or two optical metrics. Specifically, FIG. 15A illustrates anexample in which the redox ratio is utilized to classify metabolicpathways. FIG. 15B illustrates an example in which NADH bound fractionis utilized to classify metabolic pathways. FIG. 15C illustrates anexample in which mitochondrial clustering is used to classify metabolicpathways. In FIG. 15D both redox ratio and NADH bound fraction are usedto classify metabolic pathways. In FIG. 15E both NADH bound fraction andmitochondrial clustering are used to classify metabolic pathways. InFIG. 15F both redox ratio and mitochondrial clustering are used todistinguish different metabolic pathways. The classification accuracyvalues are labeled along with each approach. The term “OCA” on FIGS.15A-15F denotes original classification accuracy and the term “CVCA”denotes cross-validated classification accuracy.

FIG. 16 illustrates a table that includes the individual heterogeneityindex for each optical metric (i.e., redox ratio, bound fraction, andmitochondrial clustering) under various perturbation values. Thesignificance symbol “*” indicates significant difference compared withcorresponding control in each experiment. Differences can be evaluated,for example, using an analysis of variance (ANOVA) technique with apost-hoc Tukey HSD test to determine if there are multiple groups (e.g.,glycolysis and glutaminolysis, and β-oxidation). In certain embodiments,a two-tailed t-test can be used. In this example * represents p<0.05.

FIGS. 17A-17D-6 schematically illustrate examples of holisticvisualization of dataset using the three optical metrics describedherein. Specifically, FIG. 17A illustrates a table that includes asummary that combines changes of optical readouts under differentmetabolic perturbations. FIG. 17B includes a plot that schematicallyillustrates that a combination of these three optical metrics at thebiological replicate level can distinguish all of the metabolicpathways. FIG. 17C includes a plot that schematically illustrates that acombination of relative changes in these three optical metrics at thecell level can yield an original classification accuracy of 90.9% and across-validated classification accuracy of 90.6% in classifyingmetabolic pathways. FIGS. 17D-1 through 17D-6 include 3D scatter plotsthat have been obtained based on single cell analysis for visualizationof heterogeneity across individual cells under various metabolicperturbations. Specifically, FIGS. 17D-1 through 17D-6 include 3Dscatter plots obtained under hypoxia and glucose starvation (FIG.17D-1), chemical uncoupling (FIG. 17D-2), ex vivo cold activation (FIG.17D-3), in vivo cold activation (FIG. 17D-4), FA supplementation (FIG.17D-5, and adipogenic differentiation (FIG. 17D-6).

As shown in FIG. 17A, a combination of endogenous optical metabolicreadouts can be used to determine the presence of a metabolic changeand/or the underlying metabolic processes that produce such change. Forexample, while both enhanced glycolysis and fatty acid synthesis lead toa decrease in the optical redox ratio and an increase in mitochondrialclustering, the NADH bound fraction can decrease in the former and canincrease in the latter case. Thus, characterization of all three opticalmetabolic readouts can lead to identifying the underlying mechanismsthat drive detected metabolic changes (FIG. 17B). Further, sincecellular diversity can be one of the greatest challenges in decipheringbiological function and response to treatment, the extents to whichthese optical biomarkers can provide complementarity information can bedetermined based on the quantification of their classification potentialat the cellular level.

Single cell functional analysis can be an important factor inunderstanding the complex behavior and heterogeneity of biologicalsystems. FIG. 17C includes a graph that illustrates the results obtainedfrom analysis of metabolic readouts of 1133 randomly selected cells.Among these 1133 cells, 651 cells belong to the groups of cells exposedto perturbations that can lead to changes in glycolysis, glutaminolysis,uncoupling, and fatty acid oxidation or synthesis, and 482 belong tocontrol groups. The relative differences of each cell belonging to theperturbation groups (48-108 cells per perturbation) with respect to themean cell behavior of the control group are shown in FIG. 17C. Thethree-metric combination produces the highest separation andquantitatively yields to the highest original (90.9%) andcross-validated accuracy (90.6%) in classifying the 651 cells into theseven experimental alterations examined.

In contrast, the utilization of one or two metrics at a time can yieldvarying accuracies ranging from 33.3-66.5% (using only one metric, asshown in FIGS. 15A-15C) or 66.7-86.3% (using two metrics, as shown inFIGS. 15D-15F). FIGS. 17D-1 through 17D-6 illustrate the distributionsof these 1133 cells for each experimental perturbation, enabling aholistic visualization of multivariate optical measures of cellularfunctional heterogeneity. In the majority cases, as evident by theenlarged ellipsoids, there can be an increase in overall heterogeneityin treated groups, which can reveal cellular diversity in response toperturbations. The heterogeneity in response, as visualized by distinctchanges in the overall three dimensional orientation of the ellipsoidsand quantified by the heterogeneity index, is not always driven by thesame optical biomarker, further signifying the functionalcomplementarity of the markers. The ability to perform single cellanalysis to probe functional heterogeneity while relying on entirelyendogenous sources of contrast, within intact, live cells and tissues,dynamically over time, can offer significant advantages overtraditional, metabolic assays which are generally performed on cellextracts. Accordingly, multi-parametric two-photon imaging of endogenousmolecules and subcellular structures can enable non-invasive andquantitative assessments of metabolic alterations at the single-cell ortissue level.

FIG. 18 is a flow diagram of procedures that can be used for focusingradiation into one or more tissue segments in accordance with someembodiments disclosed herein. FIG. 19 is another flow diagram ofprocedures that can be used for focusing radiation into one or moretissue segments in accordance with some embodiments disclosed herein.

With reference to the flow diagrams of FIGS. 18 and 19, a method ofimaging tissue according to an embodiment of the present teachings caninclude focusing optical radiation (typically laser radiation) into oneor more tissue segments so as to cause a multi-photon excitation ofnicotinamide adenine dinucleotide (NADH) in the tissue and detectingfluorescence radiation emitted by excited NADH to form a raw (original)image of the tissue segment(s) 1810. NADH is intrinsically fluorescentand plays a key role in cellular energy metabolism. Moreover, NADHfluorescence intensity images are sensitive predominantly to the boundNADH form residing within mitochondria, owing to its increasedfluorescent yield in that state. Thus, NADH imaging is employed in manyembodiments of the present teachings to monitor the state ofmitochondria and their organization in-vivo.

The wavelength of illuminating radiation can be selected to excite achosen chromophore, e.g., to excite NADH. In some embodiments, thewavelength of the excitation radiation can be in a range of about 600 nmto about 1400 nm, e.g., between 700 and 800 nm.

The NADH fluorescence image can be processed to obtain a mitochondrialclustering parameter, as discussed above. With continued reference toFIGS. 18 and 19, the original image can be segmented so as to isolateintra-cellular regions of interest (mitochondria in this embodiment) andremove the other regions, e.g., via masking. By way of example, forremoval of nuclear and interstitial regions in the original image, abandpass filter can be applied to the image. In some embodiments, such abandpass filter can be created by combining three separate bandpassfilters. The first bandpass filter can be formed by multiplying aGaussian high pass filter (e.g., σ=0.01 μm−1) and a Gaussian low passfilter (e.g., σ=0.1 μm−1). The second filter can also be formed bycombining a high-pass Gaussian filter (e.g., σ=0.021 μm−1) with alow-pass Gaussian filter (σ=0.143 μm−1). The third filter can be createdby combining 3rd order Butterworth low-pass and high-pass filters. Byway of example, the frequency cut-off of the Butterworth high-passfilter can be set to 0.021 μm⁻¹ and the frequency cut-off of theButterworth low-pass filter can be set to 0.2 μm⁻¹, though other cut-offfrequencies and filter designs can also be employed. In someembodiments, the combination of Gaussian and Butterworth filters canminimize ringing artifacts in the image while providing enoughselectivity to isolate the size range of cytoplasmic image features ofinterest.

Following the application of the filter, the filtered image can betransformed into a binary mask, for example, via application of Otsu'sauto-thresholding function, composed of “bright” pixels corresponding tothe regions of interest in the image and “dark” pixels corresponding tothe discarded regions (e.g., nuclei and interstitial regions betweenboundaries of the cells). In some embodiments, additional masks may beemployed to eliminate certain unwanted regions/features of the filteredimage, such as dim image corner artifacts.

Subsequently, in this embodiment, the pixel intensities can benormalized. Such normalization can be achieved in a variety of differentways. For example, the total NADH intensity can be calculated by summingthe pixel fluorescence intensity values and the total intensity can beused to scale the pixel intensities. In some cases, the followingintensity normalization approach can be employed to minimize large scaleintensity artifacts, for example due to optical aberrations during imageacquisition. Specifically, for each identified connected componentwithin the image, which can typically include one or a few cells, thesum of the pixel intensities can be calculated. The intensity of eachparticipating pixel in each connected component can be normalized by thecorresponding intensity sum, yielding substantially uniform averageintensities for the participating components.

Subsequently, the intensity gaps (voids) in the normalized image (i.e.,the masked dark pixels) can be filled in using an automated digitalcloning (DOC) technique. For example, such a cloning technique can beemployed to fill the intensity gaps within the image produced by thenuclear and interstitial feature removal, without overwriting any brightforeground pixels, to generate a resultant image (herein also referredto as a processed image). In some embodiments, the digital cloningtechnique can be applied multiple times and the results can be averagedto obtain a resultant average image. An example of a suitable digitalcloning technique is described in an article entitled “ImprovedFourier-based characterization of intracellular fractal features,”authored by Xylas et al. in Opt. Express 20, 23442-23455 (2012), whichis herein incorporated by reference in its entirety.

A power spectral density (PSD) of the processed image can then becomputed, e.g., via obtaining Fourier transform of the image. The powerspectral density can be employed to extract a mitochondrial clusteringparameter. For example, in some embodiments, a region of the PSDcorresponding to spatial frequencies less than a selected threshold,e.g., 0.1 μm−1, can be fitted to the above Equation (1) to extract themitochondrial clustering parameter, β, where increased values of βindicates more clustered (fragmented) mitochondrial formations.

In some embodiments, the excitation radiation can excite not only achromophore of interest, e.g., NADH, but also other chromophores thatmay be potentially present, e.g., collagen, elastin, keratin andmelanin. In some such embodiments, one or more filters, e.g., Shanbhag'sentropy filter, can be applied to the original image to minimize, andpreferably eliminate, the contributions of these other chromophores tothe image signal.

In some embodiments, the above imaging method can be used to obtain themitochondrial clustering parameters at a plurality of depths of a tissueportion, e.g., the epithelium, e.g., the epidermis. In some embodiments,the depth dependence of the mitochondrial clustering parameter can thenbe employed as a predictive tool to differentiate pre-cancerous orcancerous tissue from healthy tissue. For example, it has beendiscovered that in healthy epidermal epithelia the basal and parabasallayers can display high and stable values of the clustering parameter.In particular, it has been discovered that as the epithelial celldifferentiation progresses from the basal to the higher epidermallayers, the clustering parameter shows declining values, reaching itsminimum within the spinous layer (tubular mitochondria). Further,towards the most terminal differentiation state as the granularkeratinocytes enter an apoptotic state to create the stratum corneum,the mitochondrial clustering parameter values start to recover,signifying a return to a more fissioned phenotype. In contrast, it hasbeen discovered that in diseased epithelium, e.g., epidermis, themitochondrial organization can lack depth-dependence.

FIG. 20 schematically depicts a system according to some embodimentsdisclosed herein, which includes a multi-photon microscope 10, such asthose commercially available (e.g., a multi-photon microscope marketedby JenLab Gmb under trade designation MPTflex), which can be used toobtain fluorescent images of tissue, e.g., via multiphoton excitation ofendogenous chromophores, such as NADH. An analyzer 12 can receive theimages and process those images in a manner discussed herein to obtaininformation about organization and biochemical states of one or moreselected cellular structures, e.g., the mitochondria. The analyzer canbe implemented in hardware, software, firmware or a combination thereofusing methods known in the art and supplemented in accordance with thepresent teachings.

The data used to generate the results presented in FIGS. 1D-19 wereobtained using the experimental procedures discussed below.

Cell Culture and Treatment: Primary human foreskin keratinocytes (HFKs)are cultured and exposed to either control, hypoxia or glucosestarvation conditions using protocols described in detail previously.HFKs are cultured on 50 mm glass bottom dishes (MatTek) until reachingconfluence. Two types of media are prepared for different treatments:(i) media prepared in-house with the same glucose and glutamineconcentrations as KSFM (Low Glu), or (ii) media prepared in-house withthe same glutamine concentration as KSFM, but with no glucose (No Glu).Low Glu media are prepared by adding 1 g/L of glucose (Sigma-Alrich),and 584 mg/L of L-glutamine (Sigma-Alrich) in no glucose, no glutamine,and no phenol red DMEM. No Glu media are prepared by adding 584 mg/L ofL-glutamine in no glucose, no glutamine, and no phenol red DMEM. Cellsare exposed to Low Glu media for 1 hour before imaging. For the dynamicimaging (FIGS. 4A-4C), images are taken after exposure to Low Glu media(Normal media group), Low Glu media that had been nitrogen bubbled for 6hour (Hypoxia group), or No Glu media (Glucose starvation group).Dynamic data are acquired from 3 dishes for each group. For the staticimaging (FIGS. 4D-1 through 4I), images are taken right after exposureof HFKs to Low Glu media (Normal media group), right after exposure toLow Glu media that had been nitrogen bubbled for 6 hour (Hypoxia group),or after 30 min of exposure to No Glu media (Glucose starvation group).Static data are acquired from 4 dishes for each group, with a total of16 fields per group (4 fields per dish).

Mouse HL-1 cardiomyocytes are maintained in Claycomb media(Sigma-Alrich). Images are taken right after exposure to low-dose CCCP(50 μM) or vehicle. We prepared 4 dishes for each group, and acquired atotal of 16 fields per group (4 fields per dish).

Mouse C2C12 myoblasts are maintained in DMEM supplemented with 10% fetalbovine serum. Cells are differentiated by replacing the medium with DMEMcontaining 2% horse serum. After 3 days of differentiation, C2C12 cellsexpressing the muscle marker desmin are starved for 4 hour, and thentransferred to serum-free DMEM containing 2% bovine serum albumin withor without fatty acids (Sigma-Alrich). We treated cells with theunsaturated fatty acid oleate (200 μM) or the saturated fatty acidpalmitate (200 μM). Vehicle-treated cells are used as controls. Data areacquired from 3 dishes for each group, with a total of 12 fields pergroup (4 fields per dish).

Human mesenchymal stem cells (MSCs) are cultured using a previouslyestablished method. MSCs are isolated from bone marrow aspirate andcultured in MSC proliferation medium consisting of minimum essentialmedium (MEM) α combined with 10% fetal bovine serum (FBS), 1%antibiotic/antimycotic, 1% non-essential amino acids (NEAA) and 1 ng/mLbasic fibroblastic growth factor at 37° C. with 5% CO2 in a humidifiedenvironment, until reaching confluence. Cell culture reagents arepurchased from Life Technologies (Grand Island, N.Y.) unless otherwisenoted. To induce adipogenic differentiation, Dulbecco's Modified EagleMedium with F12 nutrient mixture (DMEM/F12) was supplemented with 3%FBS, 1% antibiotic/antimycotic, human recombinant insulin (1 μM),dexamethasone (1 μM), pantothenate (17 μM), biotin (33 μM),2,3-thiazolidinediones (5 μM), and 3-isobutyl-1-methylxanthine (500 μM).Induction factors are purchased from Sigma-Alrich. Data are acquired at3 weeks post adipogenic differentiation induction. We prepared 4 dishesfor each group, and acquired a total of 8 fields per group (2 fields perdish).

Brown Adipose Tissue (BAT) Preparation: All procedures involving animaltissues are approved by the Tufts University Institutional Animal Careand Use Committee (IACUC). Twelve week old C57BL/6J male mice are housedin individual cages and acclimated at 18° C. for 2 days, followed bycold exposure at 4° C. for another 2 days with a 12-hour light-darkcycle and free access to a standard chow. A control group of mice waskept at 22-25° C. over the same period.

For in vivo imaging, BAT depots of mice are surgically exposed, underisofluorane anesthesia. There are three mice for each group, and fivefields are imaged from each depot using a 25× objective. Mouse bodytemperature was maintained with a custom heated stage. Motion artifactsare minimized by gluing the tissue to a cover-glass withcyanoacrylate-latex. After imaging, the mice are euthanized byisofluorane anesthesia followed by cervical dislocation. Upon sacrificeinterscapular BAT tissues are extracted, immediately snap frozen in dryice, and kept at −80° C. until imaging (n=3 mice per group). Two orthree tissue samples are taken from each mouse, and 6 fields per mouseare acquired from the control and the cold exposure group, respectively.

Method Details

TPEF Data Acquisition: For TPEF imaging, cell cultures are placed inhome-made micro-incubator system, which maintained 37° C. and 5% CO2within a humidified environment throughout the imaging session. BATtissue samples are placed on glass coverslips with PBS to prevent dryingwhile imaging at room temperature, and the imaging was limited within 2hours of tissue thawing. Images are obtained using a custom-builtmicroscope with a 40×water dipping objective (NA 1.1) or a 25×waterdipping objective (NA 0.95, for in vivo imaging only) equipped with atunable (710-920 nm) Ti: sapphire laser (Mai Tai; Spectra Physics;Mountain View, Calif.). Emission events are registered by aphotomultiplier tube (PMT) detector attached to a commercialtime-correlated single photon counting (TCSPC) electronics module. Toisolate NADH fluorescence, a 460(±20) nm emission filter (Chroma,ET460/40M-2P), corresponding to the NADH emission peak, was placedbefore the detector. NADH fluorescence images are acquired from this 460nm detector using 755 nm excitation. FAD fluorescence was isolated using525(±25) nm emission filter (Chroma, ET525/50M-2P) and 860 nmexcitation. For cell cultures, images (512×512 pixels; 184×184 μm) areacquired with an integration time of 60 s, using a laser power of ˜20 mWat 755 nm and ˜15 mW at 860 nm. For ex vivo BAT tissue samples, imageswith the same resolution as for cell cultures are acquired with anintegration time of 120 s, using a laser power of ˜40 mW at 755 nm and˜30 mW at 860 nm. For in vivo BAT imaging, images (512×512 pixels;294×294 μm) are acquired with an integration time of 120 s, using alaser power of −99 mW at 755 nm and −92 mW at 860 nm. The laser powerand PMT gain are recorded for each image and used to normalizefluorescence intensity.

Quantification and Statistical Analysis

Optical Redox Ratio Calculation: To process the optical redox ratio,firstly the fluorescence intensity of either NADH or FAD at each pixelwas taken as the total photon counts detected during the integrationtime without spatial binning. For cell cultures, the cytoplasm of cellswas selected based on the intensity threshold (FIG. 2F), while thesegmentation of cell cytoplasm and lipid areas for BAT tissues wasextracted by a combination of fluorescence intensity and lifetimeinformation (FIGS. 71-7F). Pixel-wise redox ratio maps are created bynormalized fluorescence intensities as: FAD/(FAD+NADH). These redoxratio maps are color-coded in MATLAB and multiplied by merged gray scaleintensity images of NADH and FAD for visualization purposes, asdemonstrated in FIGS. 4A-14F. The mean redox ratio was acquired byaveraging the redox ratio values within only the cell cytoplasm areas.

Phasor Fluorescence Lifetime Analysis: Using a commercial TCSPCelectronics module, we acquired the NADH fluorescence decay I_(m, n) ateach pixel of an image, where (m, n) is the pixel location. Then realand imaginary parts of the Fourier transform of the decay curve at eachpixel are used to determine the x and y axis coordinates of a phasor(FIG. 2D). A phasor is generally defined as a vector, whose directionrelative to the x axis represents the phase of a wave and its length theamplitude. Fluorescence lifetime spectra characterized by amono-exponential decay will map onto a point that falls on the universalsemi-circle (FIG. 2D). More complicated decay curves are represented bypoints within the semi-circle. The phasors of spectra described well bya bi-exponential decay fall on a line within the semi-circle, with thetwo points where the line intersects the semi-circle representing theshort and long lifetime components. The relative distance of the pointon that line provides an estimate of the fractional contributions of thefree (short lifetime) and bound (long lifetime) NADH components (FIG.2D). The bound NADH fraction is estimated based on the location of thecentroid of ellipses that represent the distributions of the detectedfluorescence decay data. This metric is used throughout this study toresolve NADH lifetime information. The fractional contribution can bequantified per pixel, yielding the color-coded (by MATLAB) bound NADHfraction image maps (FIG. 2E). The mean bound NADH fraction of eachimage is acquired by averaging the values within only the cell cytoplasmareas.

PSD Based Mitochondrial Clustering Characterization: To assessmitochondrial clustering, we used a Fourier technique to obtain powerspectral density (PSD) curves from each image. Briefly, the imageintensity patterns within the cell cytoplasm regions selected by binarymask (FIG. 2F) are cloned and randomly positioned in the imagebackground to create a new image without distinct cell borders and onlycell mitochondrial patterns spanning the entire image (FIG. 2G). UponFourier transform a PSD-frequency curve was created for each image. Weidentified an inverse power law behavior of PSD curve at high spatialfrequencies (>0.1 μm−1, corresponding to the size of mitochondria),suggesting a fractal organization of mitochondria and appearing as alinear portion in log-log space. We then fitted this linear portionbetween 0.1 μm−1 and the frequency at 98% of the entire PSD region(marked by asterisk, FIG. 1H) and acquired the exponential power, β,which is an indicator of the mitochondrial clustering used herein.

Cell-based Analysis: In order to assess the ability of distinguishingdifferent metabolic pathways by a combination of redox ratio, bound NADHfraction and mitochondrial clustering at cellular level, as well as theheterogeneity of these three optical metrics under differentperturbations, we did the cell based analysis complementary to the well(or animal) based one. Briefly, we randomly selected 6-8 cells from eachimage of field, and acquired the mean redox ratio, mean bound NADHfraction and mitochondrial clustering within the cytoplasm area of eachcell. Specifically, the mitochondrial clustering was calculated by clonestamping the cytoplasm area of the selected cell to create a new imagefollowed by Fourier transform. These cell based data are then groupedaccording to different perturbations for discriminant analysis (FIGS.17A-17D6, 15A-15F) or for visualization of heterogeneity (FIGS. 7D-1through 7D-6). Due to the functional and metabolic output similarities,the ex vivo and in vivo BAT cold activation data are merged into asingle group.

Calculation of Heterogeneity Index: The heterogeneity index of eachoptical biomarker was calculated. Briefly, based on the single celldata, frequency histograms are plotted for the optical redox ratio, NADHbound fraction and mitochondrial clustering. We fit the histograms toone-, two-, or three-component Gaussian curves, with the lowest AkaikeInformation Criterion indicating optimal fitting. The heterogeneityindex is defined as

H=−Σd _(i) p _(i) ln p _(i)

where i represents each Gaussian component, d represents the distancebetween the median of the Gaussian component and the median of all datawithin a biological replicate of a certain group, and p represents theproportion of this component.

The heterogeneity indices of optical biomarkers in these experiments areshown in FIG. 16, with the significance symbol revealing significantdifference compared with corresponding control.

Statistical Analysis: For samples with multiple groups (HFKs and C2C12cells), an ANOVA with post-hoc Tukey HSD test was used to assesssignificant differences in redox ratio, NADH bound fraction ormitochondrial organization using JMP 12 (SAS). Otherwise a two-tailedt-test was used. Results are considered significant at p<0.05. Forevaluating the 1, 2 or 3-metric separation models canonical lineardiscriminant analysis was performed. Discrimination accuracies arecalculated with the linear discriminant functions determined and appliedusing the entire data set as well as a leave-one-out cross validationscheme acquired by running discriminant function analysis using SPSS.

Those having ordinary skill in the art will appreciate that variouschanges can be made to the embodiments without departing from the scopeof the invention.

1.-61. (canceled)
 62. A system for optical assessment of cellularmetabolic activity, comprising an optical radiation source forilluminating at least one cell so as to cause multi-photon excitation ofat least one metabolic cofactor in the cell, wherein the metaboliccofactor emits fluorescent radiation in response to the excitation; adetector for detecting the emitted fluorescent radiation and generatinga signal indicative of the detected fluorescent radiation; and ananalyzer operating on the detector signal to provide an assessment of atleast one cellular metabolic process.
 63. The system of claim 62,wherein the optical radiation has a wavelength in a range of about 600nm to about 1400 nm.
 64. The system of claim 63, wherein the fluorescentradiation has a wavelength in a range of about 400 nm to about 650 nm.65. The system of claim 62 wherein the analyzer further comprises aprocessor for receiving the signal and operating on the signal todetermine at least one of the following parameters: (1) an intensity ofthe emitted fluorescent radiation, (2) a fluorescence lifetime of themetabolic cofactor, and (3) a parameter indicative of mitochondrialclustering in the cell.
 66. The system of claim 62, wherein the opticalradiation source provides for multi-photon excitation of at least twometabolic co-factors and the analyzer is further configured to calculatean optical redox ratio of the two cofactors based on intensities offluorescent radiation emitted by the two cofactors.
 67. The system ofclaim 62 wherein the analyzer further comprises an imaging module forperform the following steps: segmenting the image by selecting aplurality of pixels corresponding to mitochondria and masking otherpixels in the image; normalizing pixel intensities in the segmentedimage; assigning an intensity for each of the masked pixels via digitalobject cloning so as to generate a processed image; and wherein theanalyzer is further configured to obtain a Fourier transform of theprocessed image so as to determine a power spectral density associatedwith the processed image; and to compute a mitochondrial clusteringparameter using the power spectral density.
 68. The system of claim 67wherein the analyzer fits the power spectral density to an inverse powerlaw decay expression for computing the mitochondrial clusteringparameter.
 69. The system of claim 68 wherein the analyzer fits thepower spectral density to following relation: R(k)=Ak^(−β), wherein, kdenotes spatial frequency, A is an amplitude parameter, and β denotesthe mitochondrial clustering parameter.
 70. A method for imaging tissuein-vivo, comprising: illuminating a portion of a tissue in-vivo withlaser radiation so as to cause a multi-photon excitation of at least oneendogenous chromophore associated with the mitochondria, thereby causingthe endogenous chromophore to emit fluorescence radiation; detecting thefluorescence radiation and processing the detected radiation to generatea raw image of the tissue portion; segmenting the image by selecting aplurality of pixels corresponding to a cellular structure and maskingother pixels in the image; normalizing pixel intensities in thesegmented image; assigning an intensity of each of the masked pixels viadigital object cloning so as to generate a processed image; andobtaining a Fourier transform of the processed image so as to determinea power spectral density associated with the processed image.
 71. Themethod of claim 70, further comprising employing the power spectraldensity to extract information about any of biochemical state and/ororganization of the cellular structure.
 72. The method of claim 71,wherein the cellular structure is mitochondria.
 73. The method of claim72, wherein the power spectral density is employed to compute amitochondrial clustering parameter.
 74. The method of claim 73, furthercomprising fitting the power spectral density to an inverse power lawdecay expression for computing the clustering parameter.
 75. The methodof claim 74, further comprising fitting the power spectral density tofollowing relation: R(k)=Ak^(−β), wherein k denotes spatial frequency, Ais an amplitude parameter and β denotes the mitochondrial clusteringparameter.
 76. The method of claim 75, wherein the masked pixelscorrespond to pixels associated with cellular nuclei and interstitialspaces between cells in the tissue.
 77. The method of claim 70, furthercomprising applying a filter to any of the raw image and the processedimage to minimize contributions of one or more chromophores other thanthe endogenous chromophore of interest to the image.
 78. The method ofclaim 77, wherein the filter comprises Shanbhag's entropy filter. 79.The method of claim 77, wherein the endogenous chromophore of interestis NADH and the filter is employed to minimize contributions of any ofcollagen, elastin, keratin and melanin to the image.
 80. The method ofclaim 70 wherein the step of segmenting the image further comprisesapplying a at least one bandpass filter to the image.
 81. The method ofclaim 80 wherein the at least one bandpass filter is generated via acombination of Gaussian and Butterworth bandpass filters.
 82. A methodfor imaging the epithelium, the method comprising: generating aplurality of multi-photon-induced fluorescence images from a pluralityepidermal layers at different depths; and processing the images toobtain a parameter indicative of mitochondrial clustering for each ofthe epidermal depths.
 83. The method of claim 82, wherein generating thefluorescence images comprises: focusing a laser radiation at a pluralityof locations in different depths of the epithelium so as to causemulti-photon excitation of NADH in one or more cells so as to induceemission of fluorescence radiation from the excited NADH; detecting thefluorescence radiation emanating from each of the epidermal depths; andprocessing the detected radiation to generate a plurality ofmitochondrial images each corresponding to one of the depths; and foreach of the images, extracting a mitochondrial clustering parametercorresponding to one the depths.
 84. The method of claim 83, furthercomprising utilizing a depth-dependence of the mitochondrial clusteringparameter to assess whether a disease condition is present in the imagedepithelium.